Docs update (#24131)

* fix incorrect backchannel docs

`#backchannel=0` is a native rtsp source param, not an ffmpeg one, and the troubleshooting warning had it listed as ffmpeg-only. Any `#` modifier on a bare `rtsp://` source turns the backchannel off unless the URL explicitly contains `#backchannel=1`, so a dedicated two-way talk stream can't carry `#video=h264` or anything else.

* add rotation faq

.cspell/frigate-dictionary.txt

@@ -8,6 +8,7 @@ amdgpu
 analyzeduration
 Annke
 apexcharts
+Aqara
 arange
 argmax
 argmin
@@ -64,6 +65,7 @@ dsize
 dtype
 ECONNRESET
 edgetpu
+Eufy
 facenet
 fastapi
 faststart
@@ -82,6 +84,7 @@ frontdoor
 fstype
 fullchain
 fullscreen
+gatekeep
 genai
 generativeai
 genpts

.github/DISCUSSION_TEMPLATE/beta-support.yml

@@ -10,8 +10,11 @@ body:
 
         Before submitting, read the [beta documentation][docs].
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [docs]: https://docs-dev.frigate.video/
         [discussions]: https://github.com/blakeblackshear/frigate/discussions
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/camera-support.yml

@@ -8,9 +8,12 @@ body:
 
         Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/config-support.yml

@@ -8,9 +8,12 @@ body:
 
         Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/detector-support.yml

@@ -8,9 +8,12 @@ body:
 
         Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/general-support.yml

@@ -8,9 +8,12 @@ body:
 
         Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/hardware-acceleration-support.yml

@@ -8,9 +8,12 @@ body:
 
         Before submitting your support request, please [search the discussions][discussions], read the [official Frigate documentation][docs], and read the [Frigate FAQ][faq] pinned at the Discussion page to see if your question has already been answered by the community.
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/question.yml

@@ -10,9 +10,12 @@ body:
 
         **If you are looking for support, start a new discussion and use a support category.**
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: textarea
     id: description
     attributes:

.github/DISCUSSION_TEMPLATE/report-a-bug.yml

@@ -12,11 +12,14 @@ body:
 
         **If you are unsure if your issue is actually a bug or not, please submit a support request first.**
 
+        By posting here you agree to follow our [AI policy][ai-policy]. Posts that appear to be written by an AI on your behalf may be closed without a response.
+
         [discussions]: https://www.github.com/blakeblackshear/frigate/discussions
         [prs]: https://www.github.com/blakeblackshear/frigate/pulls
         [docs]: https://docs.frigate.video
         [faq]: https://github.com/blakeblackshear/frigate/discussions/12724
         [ai]: https://docs.frigate.video
+        [ai-policy]: https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
   - type: checkboxes
     attributes:
       label: Checklist

.github/ISSUE_TEMPLATE/feature_request.md

@@ -7,6 +7,13 @@ assignees: ''
 
 ---
 
+<!--
+  By posting here you agree to follow our AI policy:
+  https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md
+
+  Requests that appear to be written by an AI on your behalf may be closed without a response.
+-->
+
 **Describe what you are trying to accomplish and why in non technical terms**
 I want to be able to ... so that I can ... 
 

.github/pull_request_template.md

@@ -1,4 +1,4 @@
-_Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) before submitting a PR._
+_Please read the [contributing guidelines](https://github.com/blakeblackshear/frigate/blob/dev/CONTRIBUTING.md) and the [AI policy](https://github.com/blakeblackshear/frigate/blob/dev/AI_POLICY.md) before submitting a PR. Every PR must be read and submitted by a person, and PRs that appear to be unreviewed AI output will be closed without review._
 
 ## Proposed change
 

.gitignore

@@ -12,6 +12,7 @@ config/*
 models
 *.mp4
 *.db
+*.db-*
 *.csv
 frigate/version.py
 web/build

AI_POLICY.md

@@ -0,0 +1,126 @@
+# Frigate AI Policy
+
+## TL;DR
+
+- **Use AI tools if they help you.** We do too. This is about what you post, not which tools you use to write it.
+- **A person has to read it and send it.** Don't wire a bot or an agent up to post on your behalf.
+- **Write your posts yourself.** Your own words, the template filled in, and you answering maintainers rather than your assistant.
+- **Don't paste an AI's guess at the cause as though it were a diagnosis.** Tell us what you actually observed.
+- **Read your code before you submit it.** Disclose that AI was used, and be ready to explain every line.
+- **If we misjudge something you wrote, just say so.** We'll take you at your word.
+
+The rest of this document explains each of these, and why.
+
+## Scope
+
+AI tools are a reality of modern development and we're not opposed to their use. You are responsible for anything you submit, however it was produced, and we are responsible for anything we merge and release. We hold a high bar for both.
+
+This policy applies everywhere this project is discussed: issues, discussions, pull requests, code reviews, and commit comments.
+
+## Why this exists
+
+Frigate is built and supported by a small group of maintainers and a community of volunteers who read every post and review every pull request. Nobody here is paid to do it, and time spent reading a post is time not spent fixing bugs or building features.
+
+We're not opposed to AI tools. We use them too. But content generated by an AI and submitted without review costs a real person real time, and usually gives them less to work with than a few honest sentences would have. That is the problem this policy addresses.
+
+## A person has to be in the loop
+
+Every issue, discussion, comment, and pull request here must be read and submitted by a person. Using an AI tool to help you write is fine. Wiring one up to post on your behalf is not.
+
+Specifically, do not:
+
+- Connect a bot or agent to GitHub that opens issues, discussions, or pull requests without you reading them first
+- Post output from a tool you have not read
+- Use tooling to file bulk or drive-by contributions across the repository
+
+We will close anything we believe was posted without a person reading it, and we may mark it as spam. Posts that skip the templates are the most common sign of this.
+
+## Issues, discussions, and comments
+
+We do not mind if you use AI tools to help you write. Do not have tools post unreviewed content on your behalf. We may hide any comment we believe to be unreviewed AI output.
+
+Keep posts to what is needed to communicate your point. A long, confidently written, AI-padded post is harder to help with than a short direct one, not easier, and it is usually obvious.
+
+**Describe your actual problem in your own words.** Tell us what you did, what you expected, and what actually happened. That is the information we need, and only you have it.
+
+**Do not paste an AI's guess at the cause as though it were a diagnosis.** It is frequently wrong in ways that send everyone down the wrong path, and it buries the details that would have led to the real answer. We would rather see what you observed than what a model inferred.
+
+**Fill in the template completely.** The templates ask for logs, config, version, and hardware because those are the things needed to help you. An AI cannot supply them for you, and a post missing them cannot be acted on.
+
+**Answer maintainers yourself.** If we ask you a question, we are asking _you_, not your AI assistant. These are the spaces where we build trust and understanding with the community, and that only works if we're talking to each other. Using AI to fix your grammar or clarity is fine, but the substance has to be yours.
+
+This applies to pull request descriptions and review replies as much as it does to bug reports and discussions.
+
+### Quoting AI output
+
+If you want to include something an AI told you, it must be:
+
+- In a quote block, using `>`
+- Disclosed as AI output, saying which tool it came from
+- Accompanied by your own comment explaining why you think it is relevant
+
+Keep the excerpt short. Do not paste long transcripts.
+
+### Non-native English speakers
+
+AI is genuinely useful for participating in a project that operates in English, and we would rather hear from you through a translation tool than not hear from you at all. Using AI to improve the grammar or clarity of something you wrote yourself is fine.
+
+If you are translating your posts, make sure the translation says what you meant. Including your original text in a `<details>` block helps us verify the translation if something reads oddly, and keeps the thread readable.
+
+## Code contributions
+
+We need to understand your relationship with the code you're submitting. The more AI was involved, the more important it is that you've genuinely reviewed, tested, and understood what it produced.
+
+Because of the long-term maintenance burden every merged change creates, we require a human in the loop who understands the work the AI produced. Pull requests that appear to be unreviewed AI output will be closed without review.
+
+### Requirements when AI is used
+
+If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
+
+1. **Explicitly disclose the manner in which AI was employed.** The PR template asks for this. Be honest, this won't automatically disqualify your PR. We'd rather have an honest disclosure than find out later. Trust matters more than method.
+2. **Perform a comprehensive manual review prior to submitting the pull request.** Don't submit code you haven't read carefully and tested locally.
+3. **Be prepared to explain every line of code you submitted when asked about it by a maintainer.** If you can't explain why something works the way it does, you're not ready to submit it.
+4. **Check for an existing pull request addressing the same change.** If one exists, comment there and work with its author instead of opening a duplicate.
+5. **It is strictly prohibited to use AI to write your posts for you** (bug reports, feature requests, pull request descriptions, GitHub discussions, responding to humans, etc.). We need to hear from _you_, not your AI assistant. These are the spaces where we build trust and understanding with contributors, and that only works if we're talking to each other.
+
+### Established contributors
+
+Contributors with a long history of thoughtful, quality contributions to Frigate have earned trust through that track record. The level of scrutiny we apply to AI usage naturally reflects that trust. This isn't a formal exemption, it's just how trust works. If you've been around, we know how you think and how you work. If you're new, we're still getting to know you, and clear disclosure helps build that relationship.
+
+### What this means in practice
+
+We're not trying to gatekeep how you write code. Use whatever tools make you productive. But there's a difference between using AI as a tool to implement something you understand and handing a feature request to an AI and submitting whatever comes back. The former is fine. The latter creates maintenance risk for the project.
+
+Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term, often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
+
+One more thing worth saying directly: most maintainers already have access to the same AI tools you do. A PR that's entirely AI-generated, where the author can't explain the design, debug issues independently, or engage substantively in design discussions, doesn't offer something we couldn't produce ourselves. What makes a contribution genuinely valuable is the human judgment and domain understanding behind it, as well as the engagement during review that shapes it into something we can confidently take on long-term.
+
+## Our use of AI
+
+The Frigate documentation site has an "Ask AI" search that answers questions from the docs, and we may use AI tooling to help with triage and project management. Like any automated tooling, it is not always right.
+
+If an AI tool leaves a comment on your contribution, treat it the way you would any other comment. If you think it is wrong, say so, and a brief explanation is enough. Maintainers always have the final say.
+
+## Enforcement
+
+Contributions and posts that do not follow this policy will be closed. Depending on the situation, maintainers may also:
+
+- Hide or delete comments that appear to be unreviewed AI output
+- Mark automated content as spam
+- Close an issue, discussion, or pull request without further review
+- Lock a conversation
+- Temporarily or permanently block an account from participating in the project
+
+Repeated violations may result in being blocked from contributing to Frigate.
+
+### When we get it wrong
+
+There is no reliable way to detect this, and we're not going to pretend otherwise. Whether something reads as unreviewed AI output is a judgment call, usually made quickly, by a volunteer with limited time and no way to know for certain. These calls are subjective and we won't always get them right.
+
+If it happens to you, just say so. A short reply telling us you wrote it yourself is enough, and we'll take you at your word and pick the conversation back up. We would much rather occasionally reopen something we misjudged than treat everyone who posts here as a suspect.
+
+We'd ask for some understanding in return. These calls get made quickly because the volume is real, and time spent second-guessing them is time not spent helping the person in the next thread.
+
+## Attribution
+
+Portions of this policy are adapted from the [Open Home Foundation AI Policy](https://developers.home-assistant.io/docs/ai_policy/).

CONTRIBUTING.md

@@ -2,6 +2,8 @@
 
 Thank you for your interest in contributing to Frigate. This document covers the expectations and guidelines for contributions. Please read it before submitting a pull request.
 
+All participation in this project, including pull requests, issues, and discussions, is covered by our [AI policy](AI_POLICY.md).
+
 ## Before you start
 
 ### Bugfixes
@@ -21,28 +23,16 @@ Before writing code for a new feature:
 
 ## AI usage policy
 
-AI tools are a reality of modern development and we're not opposed to their use. But we need to understand your relationship with the code you're submitting. The more AI was involved, the more important it is that you've genuinely reviewed, tested, and understood what it produced.
+AI tools are a reality of modern development and we're not opposed to their use. But we need to understand your relationship with the code you're submitting, and we need to hear from you rather than from your AI assistant.
 
-### Requirements when AI is used
+**Read the [AI policy](AI_POLICY.md) before you open a pull request.** It is short, and it applies to everything you post here. The parts that most often catch people out:
 
-If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
+- A person has to be in the loop. Don't wire a bot or agent up to open pull requests, issues, or discussions on your behalf.
+- Disclose how AI was used. The PR template asks for this. Be honest, it won't automatically disqualify your PR.
+- Review and test everything you submit, and be prepared to explain every line when asked.
+- Don't use AI to write your PR description or your replies to maintainers.
 
-1. **Explicitly disclose the manner in which AI was employed.** The PR template asks for this. Be honest — this won't automatically disqualify your PR. We'd rather have an honest disclosure than find out later. Trust matters more than method.
-2. **Perform a comprehensive manual review prior to submitting the pull request.** Don't submit code you haven't read carefully and tested locally.
-3. **Be prepared to explain every line of code they submitted when asked about it by a maintainer.** If you can't explain why something works the way it does, you're not ready to submit it.
-4. **It is strictly prohibited to use AI to write your posts for you** (bug reports, feature requests, pull request descriptions, GitHub discussions, responding to humans, etc.). We need to hear from _you_, not your AI assistant. These are the spaces where we build trust and understanding with contributors, and that only works if we're talking to each other.
-
-### Established contributors
-
-Contributors with a long history of thoughtful, quality contributions to Frigate have earned trust through that track record. The level of scrutiny we apply to AI usage naturally reflects that trust. This isn't a formal exemption — it's just how trust works. If you've been around, we know how you think and how you work. If you're new, we're still getting to know you, and clear disclosure helps build that relationship.
-
-### What this means in practice
-
-We're not trying to gatekeep how you write code. Use whatever tools make you productive. But there's a difference between using AI as a tool to implement something you understand and handing a feature request to an AI and submitting whatever comes back. The former is fine. The latter creates maintenance risk for the project.
-
-Some honest context: when we review a PR, we're not just evaluating whether the code works today. We're evaluating whether we can maintain it, debug it, and extend it long-term — often without the original author's involvement. Code that the author doesn't deeply understand is code that nobody understands, and that's a liability.
-
-One more thing worth saying directly: most maintainers already have access to the same AI tools you do. A PR that's entirely AI-generated — where the author can't explain the design, debug issues independently, or engage substantively in design discussions — doesn't offer something we couldn't produce ourselves. What makes a contribution genuinely valuable is the human judgment and domain understanding behind it, as well as the engagement during review that shapes it into something we can confidently take on long-term.
+Pull requests that appear to be unreviewed AI output will be closed without review.
 
 ## Pull request guidelines
 

audio-labelmap.txt

@@ -24,7 +24,7 @@ yell
 sigh
 singing
 choir
-sodeling
+yodeling
 chant
 mantra
 child_singing

docker/main/Dockerfile

@@ -81,10 +81,10 @@ RUN --mount=type=bind,source=docker/main/install_tempio.sh,target=/deps/install_
 FROM base_host AS ov-converter
 ARG DEBIAN_FRONTEND
 
-# Install OpenVino Runtime and Dev library
+# Install OpenVINO for model conversion
 COPY docker/main/requirements-ov.txt /requirements-ov.txt
 RUN apt-get -qq update \
-    && apt-get -qq install -y wget python3 python3-dev python3-distutils gcc pkg-config libhdf5-dev \
+    && apt-get -qq install -y wget python3 python3-distutils \
     && wget -q https://bootstrap.pypa.io/get-pip.py -O get-pip.py \
     && sed -i 's/args.append("setuptools")/args.append("setuptools==77.0.3")/' get-pip.py \
     && python3 get-pip.py "pip" \

docker/main/build_ov_model.py

@@ -1,11 +1,106 @@
-import openvino as ov
-from openvino.tools import mo
+"""Convert the default SSDLite MobileNet v2 model to OpenVINO IR.
 
-ov_model = mo.convert_model(
-    "/models/ssdlite_mobilenet_v2_coco_2018_05_09/frozen_inference_graph.pb",
-    compress_to_fp16=True,
-    transformations_config="/usr/local/lib/python3.11/dist-packages/openvino/tools/mo/front/tf/ssd_v2_support.json",
-    tensorflow_object_detection_api_pipeline_config="/models/ssdlite_mobilenet_v2_coco_2018_05_09/pipeline.config",
-    reverse_input_channels=True,
+Replaces the legacy openvino-dev Model Optimizer conversion. The TensorFlow
+frontend translates the Object Detection API pre and post processors literally,
+producing per-class NonMaxSuppression, NonZero ops and map loops with data
+dependent shapes that the GPU plugin handles very badly. Both are cut out the
+way ssd_v2_support.json used to do it: the preprocessor is an identity at the
+native 300x300 input, and the postprocessor becomes a single fused
+DetectionOutput. The result is the [1, 1, 100, 7] tensor that Frigate's
+OpenVINO detector expects, with the input flipped to BGR to match the legacy
+reverse_input_channels behavior.
+"""
+
+import numpy as np
+import openvino as ov
+from openvino import opset8 as ops
+from openvino.preprocess import PrePostProcessor
+
+MODEL_DIR = "/models/ssdlite_mobilenet_v2_coco_2018_05_09"
+OUTPUT_PATH = "/models/ssdlite_mobilenet_v2.xml"
+INPUT_SHAPE = [1, 300, 300, 3]
+
+# faster_rcnn_box_coder divides the deltas by pipeline.config's y/x/height/width
+# scales of 10/10/5/5, which DetectionOutput expresses as per-prior variances.
+BOX_VARIANCES = np.float32([0.1, 0.1, 0.2, 0.2])
+
+model = ov.convert_model(
+    f"{MODEL_DIR}/frozen_inference_graph.pb",
+    input=[("image_tensor:0", INPUT_SHAPE)],
 )
-ov.save_model(ov_model, "/models/ssdlite_mobilenet_v2.xml")
+
+nodes = {op.get_friendly_name(): op for op in model.get_ordered_ops()}
+parameter = model.get_parameters()[0]
+
+preprocessor = nodes["Preprocessor/map/TensorArrayStack/TensorArrayGatherV3"]
+box_deltas = nodes["Postprocessor/Reshape_1"].output(0)
+class_scores = nodes["Postprocessor/convert_scores"].output(0)
+anchors_output = nodes["Postprocessor/Reshape"].output(0)
+
+# The anchors only depend on the static input shape, so fold them into a
+# constant and drop the generator subgraph with the rest of the postprocessor.
+probe = ov.Core().compile_model(
+    ov.Model([anchors_output, preprocessor.output(0)], [parameter], "probe"), "CPU"
+)
+probe_input = np.random.default_rng(0).integers(0, 255, INPUT_SHAPE, dtype=np.uint8)
+anchors, resized = (out.copy() for out in probe([probe_input]).values())
+
+assert np.allclose(resized, probe_input, atol=1e-3), (
+    "preprocessor is not an identity at 300x300, it cannot be bypassed"
+)
+
+image = ops.convert(parameter, "f32")
+
+for consumer in list(preprocessor.output(0).get_target_inputs()):
+    consumer.replace_source_output(image.output(0))
+
+# (ymin, xmin, ymax, xmax) -> (xmin, ymin, xmax, ymax)
+priors = anchors[:, [1, 0, 3, 2]].astype(np.float32).reshape(-1)
+variances = np.tile(BOX_VARIANCES, len(anchors))
+proposals = ops.constant(np.stack([priors, variances])[np.newaxis])
+
+# (ty, tx, th, tw) -> (dx, dy, dw, dh) for the CENTER_SIZE decode
+box_logits = ops.reshape(ops.gather(box_deltas, [1, 0, 3, 2], 1), [1, -1], False)
+class_preds = ops.reshape(class_scores, [1, -1], False)
+
+detections = ops.detection_output(
+    box_logits,
+    class_preds,
+    proposals,
+    {
+        "background_label_id": 0,
+        "top_k": 100,
+        "keep_top_k": [100],
+        "nms_threshold": 0.6,
+        "confidence_threshold": 0.3,
+        "code_type": "caffe.PriorBoxParameter.CENTER_SIZE",
+        "share_location": True,
+        "variance_encoded_in_target": False,
+        "normalized": True,
+        "clip_before_nms": False,
+        "clip_after_nms": True,
+        "decrease_label_id": False,
+    },
+)
+detections.output(0).get_tensor().set_names({"detection_out"})
+
+model = ov.Model([detections], [parameter], "ssdlite_mobilenet_v2")
+
+ppp = PrePostProcessor(model)
+ppp.input().tensor().set_layout(ov.Layout("NHWC"))
+ppp.input().preprocess().reverse_channels()
+model = ppp.build()
+
+# Fail the build rather than silently ship the dynamically shaped graph again.
+op_types = [op.get_type_name() for op in model.get_ordered_ops()]
+assert op_types.count("DetectionOutput") == 1, "postprocessor was not fused"
+
+for dynamic_op in ("NonMaxSuppression", "NonZero", "Loop", "TensorIterator"):
+    assert dynamic_op not in op_types, f"{dynamic_op} left in the graph"
+
+output_shape = model.outputs[0].get_partial_shape()
+assert output_shape.is_static and list(output_shape) == [1, 1, 100, 7], (
+    f"unexpected detector output shape {output_shape}"
+)
+
+ov.save_model(model, OUTPUT_PATH, compress_to_fp16=True)

docker/main/build_sqlite_vec.sh

@@ -2,7 +2,7 @@
 
 set -euxo pipefail
 
-SQLITE_VEC_VERSION="0.1.3"
+SQLITE_VEC_VERSION="0.1.9"
 
 source /etc/os-release
 

docker/main/requirements-ov.txt

@@ -1,3 +1,2 @@
 numpy
-tensorflow
-openvino-dev>=2024.0.0
+openvino >= 2026.2.0

docker/main/requirements-wheels.txt

@@ -79,7 +79,5 @@ sherpa-onnx==1.12.*
 faster-whisper==1.1.*
 librosa==0.11.*
 soundfile==0.13.*
-# DeGirum detector
-degirum == 0.16.*
 # Memory profiling
 memray == 1.15.*

docker/main/rootfs/usr/local/nginx/conf/nginx.conf

@@ -274,6 +274,13 @@ http {
                 include proxy.conf;
             }
 
+            location /api/logout {
+                auth_request off;
+                rewrite ^/api(/.*)$ $1 break;
+                proxy_pass http://frigate_api;
+                include proxy.conf;
+            }
+
             # Allow unauthenticated access to the first_time_login endpoint
             # so the login page can load help text before authentication.
             location /api/auth/first_time_login {

docs/data/object_detectors_models.yaml

@@ -1269,78 +1269,3 @@ axengine:
           input_dtype: int
           input_pixel_format: bgr
           labelmap_path: /labelmap/coco-80.txt
-degirumAiServer:
-  title: DeGirum AI Server
-  models:
-    - key: ai-server-inference
-      label: AI Server Inference
-      recommended: true
-      download: |-
-        Launch a DeGirum AI server as a Docker container, then point the detector at it. Add this to your `docker-compose.yml`:
-
-        ```yaml
-        degirum_detector:
-          container_name: degirum
-          image: degirum/aiserver:latest
-          privileged: true
-          ports:
-            - "8778:8778"
-        ```
-
-        Set `location` to the server's service name, container name, or `host:port`.
-      ui: |
-        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
-
-        | Field | Value |
-        | --- | --- |
-        | **Location** | `degirum` |
-        | **Zoo** | `degirum/public` |
-        | **Token** | your AI Hub token (optional for the public zoo) |
-      yaml: |
-        degirum_detector:
-          type: degirum
-          location: degirum
-          zoo: degirum/public
-          token: dg_example_token
-degirumLocal:
-  title: DeGirum Local
-  models:
-    - key: local-inference
-      label: Local Inference
-      recommended: true
-      download: Run hardware directly inside the Frigate container with `@local`, removing the AI server hop. The matching device runtime (e.g. the Hailo runtime) must be installed in the container; confirm it with `degirum sys-info`.
-      ui: |
-        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
-
-        | Field | Value |
-        | --- | --- |
-        | **Location** | `@local` |
-        | **Zoo** | `degirum/public` |
-        | **Token** | your AI Hub token (optional for the public zoo) |
-      yaml: |
-        degirum_detector:
-          type: degirum
-          location: @local
-          zoo: degirum/public
-          token: dg_example_token
-degirumCloud:
-  title: DeGirum AI Hub Cloud
-  models:
-    - key: ai-hub-cloud-inference
-      label: AI Hub Cloud Inference
-      recommended: true
-      download: Run inferences on DeGirum's [AI Hub](https://hub.degirum.com) cloud with `@cloud`. Sign up, create an access token, and set it as `token`. Network latency may require lowering your detection fps.
-      ui: |
-        Navigate to **Settings > System > Detectors and model** and select **DeGirum** from the detector type dropdown and click **Add**.
-
-        | Field | Value |
-        | --- | --- |
-        | **Location** | `@cloud` |
-        | **Zoo** | `degirum/public` |
-        | **Token** | your AI Hub token (optional for the public zoo) |
-      yaml: |
-        degirum_detector:
-          type: degirum
-          location: @cloud
-          zoo: degirum/public
-          token: dg_example_token

docs/docs/configuration/advanced/reference.md

@@ -11,6 +11,8 @@ It is not recommended to copy this full configuration file. Only specify values
 
 :::
 
+Sections marked `# NOTE: Can be overridden at the camera level` can be set globally and then adjusted per camera. See [Global and Camera-Level Configuration](../config_overrides.md) for how that works.
+
 ```yaml
 mqtt:
   # Optional: Enable mqtt server (default: shown below)
@@ -171,13 +173,14 @@ model:
   # Valid values are rgb, bgr, or yuv. (default: shown below)
   input_pixel_format: rgb
   # Required: Object detection model input tensor format
-  # Valid values are nhwc or nchw (default: shown below)
+  # Valid values are nhwc, nchw, hwnc, or hwcn (default: shown below)
   input_tensor: nhwc
   # Optional: Data type of the model input tensor
   # Valid values are float, float_denorm, or int (default: shown below)
   input_dtype: int
-  # Required: Object detection model type, currently only used with the OpenVINO detector
-  # Valid values are ssd, yolox, yolonas (default: shown below)
+  # Required: Object detection model architecture, used by detectors that support more
+  # than one model type (openvino, onnx, rknn, memryx, axengine, synaptics, and others)
+  # Valid values are ssd, yolox, yolonas, yolo-generic, rfdetr, dfine (default: shown below)
   model_type: ssd
   # Required: Label name modifications. These are merged into the standard labelmap.
   labelmap:
@@ -468,8 +471,8 @@ review:
     detections: False
     # Optional: Activity Context Prompt to give context to the GenAI what activity is and is not suspicious.
     # It is important to be direct and detailed. See documentation for the default prompt structure.
-    activity_context_prompt: """Define what is and is not suspicious
-"""
+    activity_context_prompt: |
+      Define what is and is not suspicious
     # Optional: Image source for GenAI (default: preview)
     # Options: "preview" (uses cached preview frames at ~180p) or "recordings" (extracts frames from recordings at 480p)
     # Using "recordings" provides better image quality but uses more tokens per image.
@@ -813,7 +816,8 @@ classification:
         cameras:
           camera_name:
             # Required: Crop of image frame on this camera to run classification on
-            crop: [0, 180, 220, 400]
+            # [x1, y1, x2, y2] as decimals between 0 and 1, relative to the detect resolution
+            crop: [0.0, 0.25, 0.3, 0.85]
         # Optional: If classification should be run when motion is detected in the crop (default: shown below)
         motion: False
         # Optional: Interval to run classification on in seconds (default: shown below)
@@ -977,7 +981,9 @@ cameras:
       # Optional: Adjust sort order of cameras in the UI. Larger numbers come later (default: shown below)
       # By default the cameras are sorted alphabetically.
       order: 0
-      # Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
+      # Optional: Whether or not to show the camera on the default All Cameras live dashboard.
+      # The camera is still available everywhere else, including camera groups and settings
+      # (default: shown below)
       dashboard: True
       # Optional: Whether this camera is visible in review (the review page and its camera
       # filter, motion review, and the history view) (default: shown below)

docs/docs/configuration/advanced/system.md

@@ -67,6 +67,12 @@ This section can be used to set environment variables for those unable to modify
 
 Variables prefixed with `FRIGATE_` can be referenced in config fields that support environment variable substitution (such as MQTT host and credentials, camera stream URLs, and ONVIF host and credentials) using the `{FRIGATE_VARIABLE_NAME}` syntax.
 
+:::note
+
+The `go2rtc` section is an exception. go2rtc runs as a separate process, so its stream definitions can only be substituted with variables that exist in the container's environment (set via Docker `-e`, the `environment:` section of `docker-compose.yml`, or Docker secrets). Variables defined in the `environment_vars` block above are not available to go2rtc streams. Home Assistant app users, who cannot set container environment variables, must instead put credentials directly in their go2rtc stream URLs.
+
+:::
+
 <ConfigTabs>
 <TabItem value="ui">
 
@@ -287,6 +293,10 @@ networking:
 
 This setting is for advanced users. For the majority of use cases it's recommended to change the `ports` section of your Docker compose file or use the Docker `run` `--publish` option instead, e.g. `-p 443:8971`. Changing Frigate's ports may break some integrations.
 
+The internal and external ports must be different port numbers, and Frigate will refuse to start otherwise. Requests arriving on the internal port are treated as authenticated admins, so pointing both at the same port would remove authentication from the external one.
+
+Nginx binds these ports when it starts, so port changes only take effect after Frigate restarts.
+
 :::
 
 ### Customizing the Nginx configuration
@@ -329,7 +339,7 @@ For example:
 ```
 services:
   frigate:
-    image: blakeblackshear/frigate:latest
+    image: ghcr.io/blakeblackshear/frigate:stable
     environment:
       - FRIGATE_BASE_PATH=/frigate
 ```

docs/docs/configuration/audio_detectors.md

@@ -256,7 +256,7 @@ The only field that is valid at the camera level is `enabled`.
 
 #### Live transcription
 
-The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate/<camera_name>/audio/transcription` will also be updated in real-time with transcribed text.
+The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the `audio` role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing, or toggle it outside of the UI with the [`frigate/<camera_name>/audio_transcription/set`](/integrations/mqtt#frigatecamera_nameaudio_transcriptionset) MQTT topic or the HTTP API. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic `frigate/<camera_name>/audio/transcription` will also be updated in real-time with transcribed text.
 
 Results can be error-prone due to a number of factors, including:
 
@@ -272,7 +272,7 @@ If you have CUDA hardware, you can experiment with the `large` `whisper` model o
 
 #### Transcription and translation of `speech` audio events
 
-Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button in the Tracked Object Details pane.
+Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button (the microphone icon) in the Tracked Object Details pane.
 
 In order to use transcription and translation for past events, you must enable audio detection and define `speech` as an audio type to listen for. To have `speech` events translated into the language of your choice, set the `language` config parameter with the correct [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
 
@@ -294,7 +294,7 @@ Recorded `speech` events will always use a `whisper` model, regardless of the `m
 
    Because transcription is **serialized (one event at a time)** and speech events can be generated far faster than they can be processed, an auto-transcribe toggle would very quickly create an ever-growing backlog and degrade core functionality. For the amount of engineering and risk involved, it adds **very little practical value** for the majority of deployments, which are often on low-powered, edge hardware.
 
-   If you hear speech that's actually important and worth saving/indexing for the future, **just press the transcribe button in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
+   If you hear speech that's actually important and worth saving/indexing for the future, **just press the transcribe button (the microphone icon) in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
 
    Other options are being considered for future versions of Frigate to add transcription options that support external `whisper` Docker containers. A single transcription service could then be shared by Frigate and other applications (for example, Home Assistant Voice), and run on more powerful machines when available.
 

docs/docs/configuration/authentication.md

@@ -262,6 +262,19 @@ In this example:
 
 - Admin precedence: if the `admin` mapping matches, Frigate resolves the session to `admin` to avoid accidental downgrade when a user belongs to multiple groups (for example both `admin` and `viewer` groups).
 
+:::note
+
+If a user isn't getting the role you expect, enable debug logging to see exactly what headers Frigate is receiving from your proxy:
+
+```yaml
+logger:
+  default: info
+  logs:
+    frigate.api.auth: debug
+```
+
+:::
+
 #### Port Considerations
 
 **Authenticated Port (8971)**

docs/docs/configuration/camera_specific.md

@@ -165,7 +165,7 @@ If available, recommended settings are:
 
 #### Setup via the Add Camera Wizard
 
-The Add Camera Wizard is the recommended way to add a standard Reolink camera. Before starting, make sure [HTTP is enabled](https://support.reolink.com/articles/360003452893-How-to-Access-Reolink-Cameras-NVRs-Home-Hub-Locally-via-Web-Browsers/) in the camera's advanced network settings. The wizard uses the camera's HTTP API to determine its resolution and choose the recommended stream type from the table above.
+The [Add Camera Wizard](cameras.md#adding-a-camera-with-the-add-camera-wizard) is the recommended way to add a standard Reolink camera. Before starting, make sure [HTTP is enabled](https://support.reolink.com/articles/360003452893-How-to-Access-Reolink-Cameras-NVRs-Home-Hub-Locally-via-Web-Browsers/) in the camera's advanced network settings. The wizard uses the camera's HTTP API to determine its resolution and choose the recommended stream type from the table above.
 
 1. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />.
 2. Choose **Manual selection** as the stream detection method and select **Reolink** as the camera brand.

docs/docs/configuration/cameras.md

@@ -7,6 +7,74 @@ import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
 
+## Adding a camera with the Add Camera Wizard
+
+The Add Camera Wizard is the recommended way to add a camera. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />. The wizard connects to your camera, tests each stream, and writes the camera's configuration for you, including the [go2rtc](go2rtc.md) restream and the live view stream mapping, so a standard setup needs no hand-written YAML.
+
+### Step 1: Name and connection
+
+Enter a name for the camera along with its host or IP address and credentials, then choose how the wizard should find the camera's streams:
+
+- **Probe camera** queries the camera over ONVIF (the ONVIF port is usually 80 or 8080) and asks it for its stream URLs. Some cameras use a separate ONVIF/service account rather than the device admin user, and some require **Use digest authentication** to be enabled.
+- **Manual selection** builds a stream URL from a template for the camera brand you pick (Dahua/Amcrest/EmpireTech, Hikvision/Uniview/Annke, Ubiquiti, Reolink, Axis, TP-Link, or Foscam). Choose **Other** to enter a custom RTSP URL directly. Non-RTSP stream types must be [configured manually](#setting-up-camera-inputs).
+
+The name you enter is lowercased and spaces become underscores. If the result still isn't a valid config key, the wizard generates a safe name and stores what you typed as `friendly_name`.
+
+### Step 2: Probe or snapshot
+
+In probe mode, the wizard reports what the camera returned (manufacturer, model, firmware, profile count, and whether PTZ, presets, and [autotracking](autotracking.md) are supported) along with the RTSP URLs it discovered. Test each candidate to see its resolution, frame rate, and codecs together with a snapshot, then select the one you want to use.
+
+In manual mode, the wizard tests the templated URL and shows the same metadata and snapshot.
+
+If no RTSP URLs are found, the credentials may be wrong or the camera may not support ONVIF. Go back and use manual selection instead.
+
+### Step 3: Stream configuration
+
+Assign [roles](#setting-up-camera-inputs) to the stream, and use **Add Another Stream** to add the camera's other streams, for example a substream for `detect` alongside the main stream for `record`. At least one stream must have the `detect` role before you can continue.
+
+**Reduce connections to camera** routes that input through the go2rtc restream so Frigate and the live view share a single connection to the camera instead of each opening their own. See [restream](restream.md) for more detail.
+
+### Step 4: Validation and testing
+
+Connect each stream to get a live preview, an estimated bandwidth figure, and a list of validation results. The wizard checks for the most common misconfigurations, including:
+
+- A detect resolution that is too high (increased resource usage) or too low for reliable detection, or one it could not probe at all
+- A stream marked `record` whose audio codec is not AAC, or that has no audio at all
+- A stream marked `audio` that carries no audio stream
+- Using a restreamed input for the `record` role
+- Brand-specific issues, such as an RTSP stream on a Reolink camera that should use http-flv, or a Dahua/Hikvision substream selected for `detect`
+
+**Use stream compatibility mode** passes the stream through go2rtc's ffmpeg module. Enable it if a stream fails to load after several attempts. Note that this also prevents [two way talk](/configuration/live#two-way-talk) from being detected for that stream.
+
+**Save New Camera** writes the configuration and starts the camera right away. No restart is required.
+
+Other features, including [hardware acceleration](hardware_acceleration_video.md), [two way talk](/configuration/live#two-way-talk), and audio transcoding, is configured after the camera has been added. For camera model specific quirks, see the [camera specific](camera_specific.md) docs.
+
+## Deleting a camera
+
+Click **Delete Camera** in <NavPath path="Settings > Global configuration > Camera management" />, choose the camera, and confirm. Deleting a camera requires the `admin` role and cannot be undone.
+
+:::warning
+
+Deleting a camera permanently removes its recordings, tracked objects, and configuration. If you only want to stop processing a camera, set its state to **Off** or **Disabled** in <NavPath path="Settings > Global configuration > Camera management" /> instead. See [camera state](/configuration/live#camera-state).
+
+:::
+
+Deleting a camera removes:
+
+- The camera's section of your config file, along with its entries in any [role](authentication.md#user-roles) camera list. A custom role left with no cameras is removed as well.
+- Every database record for the camera: tracked objects, review items, recordings, previews, timeline entries, the saved region grid, and [triggers](semantic_search.md#triggers).
+- Every media file for the camera: recordings, snapshots, thumbnails, and preview clips.
+
+[Exports](/usage/exports) are kept by default, so saved footage survives the deletion of the camera it came from. Turn on **Also delete exports for this camera** in the confirmation step to remove those too.
+
+The camera's processes are stopped and the change takes effect immediately, so no restart is required. If the resulting config cannot be parsed, Frigate restores the previous config and reports an error instead of leaving Frigate in a broken state.
+
+Two things are not cleaned up for you:
+
+- **go2rtc streams.** Frigate makes a best effort to stop a running [go2rtc](go2rtc.md) stream named after the camera, but stream entries in your config file remain and are recreated on the next restart. Remove them in <NavPath path="Settings > System > go2rtc streams" /> or in your config file.
+- **Camera groups.** A deleted camera stays listed in any [camera group](#setting-up-camera-groups) that referenced it. The group skips the missing camera, so this is harmless, but you can edit the group to drop the stale entry.
+
 ## Setting Up Camera Inputs
 
 Several inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create recordings from a higher resolution stream, or vice versa.
@@ -69,7 +137,7 @@ Additional cameras are simply added under the camera configuration section.
 <ConfigTabs>
 <TabItem value="ui">
 
-Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and use the add camera button to configure each additional camera.
+Navigate to <NavPath path="Settings > Global configuration > Camera management" /> and use the [Add Camera Wizard](#adding-a-camera-with-the-add-camera-wizard) to configure each additional camera.
 
 </TabItem>
 <TabItem value="yaml">

docs/docs/configuration/config.md

@@ -20,7 +20,7 @@ Settings are organized into two scopes:
 - **Global configuration**: values under <NavPath path="Settings > Global configuration" /> apply to every camera by default. This is where you set the baseline behavior for object detection, recording, snapshots, motion, and so on.
 - **Camera configuration**: values under <NavPath path="Settings > Camera configuration" /> apply to a single camera. Use the camera selector button at the top of these pages to choose which camera you are editing.
 
-When a camera-level section is left untouched, the camera simply inherits the global values. Changing a value on a camera page **overrides** the global value for that camera only: the global setting and every other camera are unaffected. This mirrors how the YAML works, where a value set under `cameras.<name>` takes precedence over the same value set at the top level.
+When a camera-level section is left untouched, the camera simply inherits the global values. Changing a value on a camera page **overrides** the global value for that camera only: the global setting and every other camera are unaffected. This mirrors how the YAML works, where a value set under `cameras.<name>` takes precedence over the same value set at the top level. See [Global and Camera-Level Configuration](./config_overrides.md) for the full details, including how lists and maps are handled and which settings must be enabled globally first.
 
 To undo an override and go back to inheriting from the parent scope, use the reset button at the bottom of the section:
 
@@ -130,7 +130,8 @@ go2rtc:
 
 ```yaml
 genai:
-  api_key: "{FRIGATE_GENAI_API_KEY}"
+  my_provider:
+    api_key: "{FRIGATE_GENAI_API_KEY}"
 ```
 
 ## Common configuration examples

docs/docs/configuration/config_overrides.md

@@ -0,0 +1,244 @@
+---
+id: config_overrides
+title: Global and Camera-Level Configuration
+---
+
+import ConfigTabs from "@site/src/components/ConfigTabs";
+import TabItem from "@theme/TabItem";
+import NavPath from "@site/src/components/NavPath";
+
+Most of Frigate's configuration can be set once for all cameras and then adjusted for individual cameras. The global value acts as the default for every camera, and any camera can override it.
+
+This page explains how that inheritance works. For a tour of the Settings UI itself, see [Frigate Configuration](./config.md).
+
+## The basics
+
+Set a value globally and every camera uses it. Set the same value on a camera and that camera uses its own value instead.
+
+<ConfigTabs>
+<TabItem value="ui">
+
+1. Navigate to <NavPath path="Settings > Global configuration > Object detection" /> and set **Detect FPS** to `5`. Every camera now detects at 5 fps.
+2. Navigate to <NavPath path="Settings > Camera configuration > Object detection" />, select the `driveway` camera, and set **Detect FPS** to `10`.
+
+The `driveway` camera now detects at 10 fps. Every other camera still uses the global value of 5.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+detect:
+  fps: 5 # every camera detects at 5 fps
+
+cameras:
+  front_door:
+    ffmpeg: ...
+  driveway:
+    ffmpeg: ...
+    detect:
+      fps: 10 # except this one
+```
+
+`front_door` inherits `fps: 5`, and `driveway` uses `10`.
+
+</TabItem>
+</ConfigTabs>
+
+## Overrides apply per value, not per section
+
+Overriding one value in a section does not detach the rest of that section. Everything you don't set on the camera still comes from the global configuration.
+
+<ConfigTabs>
+<TabItem value="ui">
+
+If you set a camera's **Motion threshold** but leave **Contour area** alone, only the threshold is overridden. The contour area continues to follow <NavPath path="Settings > Global configuration > Motion detection" />, and changing it there still affects that camera.
+
+Open a section to see which values are overridden: the section header indicates how many fields differ from the global configuration.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+motion:
+  threshold: 30
+  contour_area: 10
+
+cameras:
+  driveway:
+    motion:
+      threshold: 40
+```
+
+The `driveway` camera ends up with `threshold: 40` and `contour_area: 10`. Only the value you wrote was overridden.
+
+</TabItem>
+</ConfigTabs>
+
+## Returning a camera to the global value
+
+<ConfigTabs>
+<TabItem value="ui">
+
+A camera section that has its own values shows an **Overridden** badge. To remove the override and go back to inheriting, use the **Reset to Global** button at the bottom of the section.
+
+</TabItem>
+<TabItem value="yaml">
+
+Frigate treats a camera value as an override because it is written in the config file, not because it differs from the global value. Repeating the global value under a camera still creates an override:
+
+```yaml
+snapshots:
+  enabled: true
+
+cameras:
+  driveway:
+    snapshots:
+      enabled: true # this is an override, even though it matches
+```
+
+If you later change the global `snapshots.enabled` to `false`, `driveway` keeps saving snapshots, because it has its own value. To make a camera follow the global value again, delete the key from the camera rather than setting it to match.
+
+</TabItem>
+</ConfigTabs>
+
+## Lists replace, maps merge
+
+This is the distinction that surprises people most.
+
+**Lists are replaced entirely.** A camera's list does not add to the global list, it takes its place.
+
+<ConfigTabs>
+<TabItem value="ui">
+
+The camera page shows the objects the camera is currently tracking, starting from the global list. Changing that selection under <NavPath path="Settings > Camera configuration > Objects" /> replaces the list for that camera, so make sure every object you want tracked is selected, not just the ones you are adding.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+objects:
+  track:
+    - person
+    - car
+
+cameras:
+  backyard:
+    objects:
+      track:
+        - dog # backyard tracks ONLY dog, not person or car
+```
+
+To track `dog` in addition to the global objects, list all of them on the camera.
+
+</TabItem>
+</ConfigTabs>
+
+An empty list is a valid override, and is the normal way to opt a camera out of something:
+
+```yaml
+review:
+  alerts:
+    labels:
+      - person
+
+cameras:
+  street:
+    review:
+      alerts:
+        labels: [] # this camera never creates alerts
+```
+
+**Maps are merged key by key.** A camera can add an entry without redeclaring the others.
+
+<ConfigTabs>
+<TabItem value="ui">
+
+Adding a filter for one object under <NavPath path="Settings > Camera configuration > Objects" /> does not remove the filters inherited from <NavPath path="Settings > Global configuration > Objects" />. The camera keeps both.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+objects:
+  filters:
+    person:
+      min_area: 5000
+
+cameras:
+  driveway:
+    objects:
+      filters:
+        car:
+          min_area: 10000
+```
+
+The `driveway` camera ends up with both the `car` filter it defined and the `person` filter from the global configuration.
+
+</TabItem>
+</ConfigTabs>
+
+## Which settings can be overridden
+
+Most, but not all. The [full reference config](./advanced/reference.md) is the authoritative source: sections that support camera-level overrides are marked with the comment `# NOTE: Can be overridden at the camera level`. In the UI, a setting can be overridden if it appears under both <NavPath path="Settings > Global configuration" /> and <NavPath path="Settings > Camera configuration" />.
+
+A few things worth knowing beyond that:
+
+- Some sections are **global only** and have no camera-level equivalent, including `go2rtc`, `genai` providers, `classification`, `telemetry`, `camera_groups`, and `ui`.
+- Some sections exist **only at the camera level**, such as `zones` and `onvif`.
+- Some sections are **partially overridable**, meaning a camera accepts only a few of the keys available globally. `face_recognition`, `lpr`, and `audio_transcription` work this way, and the reference config notes which keys apply.
+
+## Enrichments that must be enabled globally first
+
+License plate recognition and face recognition are special: the global setting is not just a default, it is a switch that must be on before any camera can use the feature. Enabling one on a camera while it is disabled globally is a configuration error, and Frigate will refuse to start:
+
+```
+Camera driveway has lpr enabled but lpr is disabled at the global level of the config. You must enable lpr at the global level.
+```
+
+Enable the feature globally, then turn it off on the cameras that don't need it.
+
+<ConfigTabs>
+<TabItem value="ui">
+
+1. Navigate to <NavPath path="Settings > Global configuration > License plate recognition" /> and enable **LPR**.
+2. Navigate to <NavPath path="Settings > Camera configuration > License plate recognition" />, select each camera that should not run LPR, and disable the **Enable LPR** toggle.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+lpr:
+  enabled: true
+
+cameras:
+  driveway:
+    ffmpeg: ... # inherits lpr, enabled
+  backyard:
+    ffmpeg: ...
+    lpr:
+      enabled: false # opted out
+```
+
+</TabItem>
+</ConfigTabs>
+
+:::note
+
+This applies only to `lpr` and `face_recognition`, because the global setting controls whether the supporting background process starts at all. Other features do not work this way. Audio transcription, for example, can be enabled on a single camera without being enabled globally.
+
+:::
+
+## Profiles
+
+[Profiles](./profiles.md) add a further layer on top of everything described above. A profile is a named set of camera overrides that you can switch on and off while Frigate is running, for example to change detection and recording behavior when you leave the house.
+
+Profiles are applied on top of a camera's already-resolved configuration, so a profile value wins over both the camera and the global value while that profile is active. Profiles cover a subset of the camera sections and do not modify your config file.
+
+## Summary
+
+- A camera inherits every value you don't set on it.
+- Overriding one value does not detach the rest of the section.
+- Writing a value on a camera overrides it, even if it matches the global value. Remove it to inherit again.
+- Lists replace the global list. Maps merge into it.
+- An empty list is an override, not an omission.
+- `lpr` and `face_recognition` must be enabled globally before a camera can use them.

docs/docs/configuration/custom_classification/object_classification.md

@@ -11,7 +11,7 @@ Object classification allows you to train a custom MobileNetV2 classification mo
 
 :::info
 
-Training a custom object classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+Training a custom object classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
 
 :::
 

docs/docs/configuration/custom_classification/state_classification.md

@@ -11,7 +11,7 @@ State classification allows you to train a custom MobileNetV2 classification mod
 
 :::info
 
-Training a custom state classification model requires a one-time internet connection to download MobileNetV2 base weights. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
+Training a custom state classification model requires an internet connection to download MobileNetV2 base weights. By default these weights are not cached in `/config/`, so they are downloaded again after the container is recreated. Once trained, the model runs fully offline. See [Network Requirements](/frigate/network_requirements#one-time-model-downloads) for details.
 
 :::
 
@@ -73,9 +73,13 @@ classification:
         interval: 10 # also run every N seconds (optional)
         cameras:
           front:
-            crop: [0, 180, 220, 400]
+            # [x1, y1, x2, y2] as decimals between 0 and 1, relative to the
+            # camera's detect resolution
+            crop: [0.0, 0.25, 0.3, 0.85]
 ```
 
+Crop coordinates are normalized: each value is a fraction of the camera's `detect` width or height, not a pixel value. Drawing the crop in the UI wizard writes these values for you.
+
 An optional config, `save_attempts`, can be set as a key under the model name. This defines the number of classification attempts to save in the Recent Classifications tab. For state classification models, the default is 100.
 
 </TabItem>

docs/docs/configuration/face_recognition.md

@@ -232,7 +232,21 @@ Once front-facing images are performing well, start choosing slightly off-angle
 
 Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.
 
-1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
+1. Enable debug logs to see exactly what Frigate is doing.
+   - Enable debug logs for face recognition by adding `frigate.data_processing.real_time.face: debug` to your `logger` configuration. Restart Frigate after this change.
+
+     ```yaml
+     logger:
+       default: info
+       logs:
+         # highlight-next-line
+         frigate.data_processing.real_time.face: debug
+     ```
+
+   - These logs report where the pipeline stopped for each `person` object, such as no face being found within the person's bounding box, the detected face being smaller than `min_area`, or a face being recognized but scoring too low.
+   - If you see no face-related messages at all, also add `frigate.embeddings.maintainer: debug` to confirm that the face processor was created at startup and that `person` updates are reaching it.
+
+2. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
 
    If you are using a Frigate+ or `face` detecting model:
    - Watch the [debug view](/usage/live#the-single-camera-view) to ensure that `face` is being detected along with `person`.
@@ -242,7 +256,7 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
    - Check your `detect` stream resolution and ensure it is sufficiently high enough to capture face details on `person` objects.
    - You may need to lower your `detection_threshold` if faces are not being detected.
 
-2. Any detected faces will then be _recognized_.
+3. Any detected faces will then be _recognized_.
    - Make sure you have trained at least one face per the recommendations above.
    - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 

docs/docs/configuration/genai/config.md

@@ -6,12 +6,46 @@ title: Configuring Generative AI
 import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
 
 ## Configuration
 
-A Generative AI provider can be configured in the global config, which will make the Generative AI features available for use. There are currently 4 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI-Compatible section below.
+A Generative AI provider can be configured in the global config, which will make the Generative AI features available for use. There are currently 5 native providers available to integrate with Frigate. Other providers that support the OpenAI standard API can also be used. See the OpenAI-Compatible section below.
 
-To use Generative AI, you must define a single provider at the global level of your Frigate configuration. If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
+`genai` is a map of named providers. Each key under `genai` is a name you choose, and its value is that provider's settings:
+
+<ConfigTabs>
+<TabItem value="ui">
+
+1. Navigate to <NavPath path="Settings > Enrichments > Generative AI" />.
+   - Click **Add** and enter a **Provider name**. Any name of letters, numbers, hyphens, and underscores is accepted, but it cannot be changed from the UI after the provider is created.
+   - Set **Provider** to the service you are using (e.g., `ollama`)
+   - Set **Base URL**, **API key**, and **Model** as required by that provider
+   - Set **Roles** to the roles this provider should handle.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+genai:
+  my_provider: # any name you like
+    provider: ollama
+    base_url: http://localhost:11434
+    model: qwen3-vl:4b
+    roles:
+      - descriptions
+      - embeddings
+      - chat
+```
+
+</TabItem>
+</ConfigTabs>
+
+The examples on this page all use `my_provider`, but the name is arbitrary and is only used to reference the provider elsewhere in the config (for example, `semantic_search.model`).
+
+Each provider handles one or more **roles**: `chat`, `descriptions`, and `embeddings`. A provider handles all three by default, and each role may be assigned to exactly one provider. Define a single provider if you want it to do everything, or split the roles across several providers using the `roles` option.
+
+If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
 
 ## Local Providers
 
@@ -25,14 +59,23 @@ Running Generative AI models on CPU is not recommended, as high inference times
 
 ### Recommended Local Models
 
-You must use a vision-capable model with Frigate. The following models are recommended for local deployment:
+#### Vision models
 
-| Model      | Notes                                                                                                                                                                |
-| ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `qwen3-vl` | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
-| `qwen3.5`  | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
-| `qwen3.6`  | Strong situational understanding, similar to qwen3-vl                                                                                                                |
-| `gemma4`   | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action.                                     |
+You must use a vision-capable model with Frigate. The following models are recommended for local deployment of the `descriptions` and `chat` roles:
+
+| Model               | Notes                                                                                                                                                                |
+| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `qwen3-vl`          | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
+| `qwen3.6`/`qwen3.8` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
+| `gemma4`            | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action.                                     |
+
+#### Embedding models
+
+The `embeddings` role needs a different kind of model. Text queries are matched against the stored image embeddings, so the model must be trained to place images and text into the same vector space. A chat or description model will still return vectors when asked, but those vectors are not trained for retrieval and text searches will return poor matches with no error to indicate why.
+
+| Model                | Notes                                                                                                                                                               |
+| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Must be served by llama.cpp started with `--embeddings` and `--mmproj`. |
 
 :::info
 
@@ -78,23 +121,26 @@ All llama.cpp native options can be passed through `provider_options`, including
    - Set **Provider** to `llamacpp`
    - Set **Base URL** to your llama.cpp server address (e.g., `http://localhost:8080`)
    - Set **Model** to the name of your model
-   - Under **Provider Options**, set `context_size` to tell Frigate your context size so it can send the appropriate amount of information
+   - Optionally, under **Provider Options**, set `context_size` to override the context size Frigate detects from the server
 
 </TabItem>
 <TabItem value="yaml">
 
 ```yaml
 genai:
-  provider: llamacpp
-  base_url: http://localhost:8080
-  model: your-model-name
-  provider_options:
-    context_size: 16000 # Tell Frigate your context size so it can send the appropriate amount of information.
+  my_provider:
+    provider: llamacpp
+    base_url: http://localhost:8080
+    model: your-model-name
+    provider_options:
+      context_size: 16000 # Optional, overrides the context size reported by the server.
 ```
 
 </TabItem>
 </ConfigTabs>
 
+Frigate queries the llama.cpp server for the model's context size at startup and logs it along with the other detected capabilities. If `context_size` is set in `provider_options`, that value is always used instead, even when the server reports its own.
+
 ### Ollama
 
 [Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
@@ -127,13 +173,14 @@ Note that Frigate will not automatically download the model you specify in your
 
 ```yaml
 genai:
-  provider: ollama
-  base_url: http://localhost:11434
-  model: qwen3-vl:4b
-  provider_options: # other Ollama client options can be defined
-    keep_alive: -1
-    options:
-      num_ctx: 8192 # make sure the context matches other services that are using ollama
+  my_provider:
+    provider: ollama
+    base_url: http://localhost:11434
+    model: qwen3-vl:4b
+    provider_options: # other Ollama client options can be defined
+      keep_alive: -1
+      options:
+        num_ctx: 8192 # make sure the context matches other services that are using ollama
 ```
 
 </TabItem>
@@ -149,11 +196,12 @@ For OpenAI-compatible servers (such as llama.cpp) that don't expose the configur
 
 ```yaml
 genai:
-  provider: openai
-  base_url: http://your-llama-server
-  model: your-model-name
-  provider_options:
-    context_size: 8192 # Specify the configured context size
+  my_provider:
+    provider: openai
+    base_url: http://your-llama-server
+    model: your-model-name
+    provider_options:
+      context_size: 8192 # Specify the configured context size
 ```
 
 This ensures Frigate uses the correct context window size when generating prompts.
@@ -176,10 +224,11 @@ This ensures Frigate uses the correct context window size when generating prompt
 
 ```yaml
 genai:
-  provider: openai
-  base_url: http://your-server:port
-  api_key: your-api-key # May not be required for local servers
-  model: your-model-name
+  my_provider:
+    provider: openai
+    base_url: http://your-server:port
+    api_key: your-api-key # May not be required for local servers
+    model: your-model-name
 ```
 
 </TabItem>
@@ -217,19 +266,21 @@ Ollama also supports [cloud models](https://ollama.com/cloud), where model infer
 
 ```yaml
 genai:
-  provider: ollama
-  base_url: http://localhost:11434
-  model: cloud-model-name
+  my_provider:
+    provider: ollama
+    base_url: http://localhost:11434
+    model: cloud-model-name
 ```
 
 or when using Ollama Cloud directly
 
 ```yaml
 genai:
-  provider: ollama
-  base_url: https://ollama.com
-  model: cloud-model-name
-  api_key: your-api-key
+  my_provider:
+    provider: ollama
+    base_url: https://ollama.com
+    model: cloud-model-name
+    api_key: your-api-key
 ```
 
 </TabItem>
@@ -267,9 +318,10 @@ To start using Gemini, you must first get an API key from [Google AI Studio](htt
 
 ```yaml
 genai:
-  provider: gemini
-  api_key: "{FRIGATE_GEMINI_API_KEY}"
-  model: gemini-2.5-flash
+  my_provider:
+    provider: gemini
+    api_key: "{FRIGATE_GEMINI_API_KEY}"
+    model: gemini-2.5-flash
 ```
 
 </TabItem>
@@ -279,12 +331,13 @@ genai:
 
 To use a different Gemini-compatible API endpoint, set the `provider_options` with the `base_url` key to your provider's API URL. For example:
 
-```yaml {4,5}
+```yaml {5,6}
 genai:
-  provider: gemini
-  ...
-  provider_options:
-    base_url: https://...
+  my_provider:
+    provider: gemini
+    ...
+    provider_options:
+      base_url: https://...
 ```
 
 Other HTTP options are available, see the [python-genai documentation](https://github.com/googleapis/python-genai).
@@ -318,9 +371,10 @@ To start using OpenAI, you must first [create an API key](https://platform.opena
 
 ```yaml
 genai:
-  provider: openai
-  api_key: "{FRIGATE_OPENAI_API_KEY}"
-  model: gpt-4o
+  my_provider:
+    provider: openai
+    api_key: "{FRIGATE_OPENAI_API_KEY}"
+    model: gpt-4o
 ```
 
 </TabItem>
@@ -336,13 +390,14 @@ To use a different OpenAI-compatible API endpoint, set the `OPENAI_BASE_URL` env
 
 For OpenAI-compatible servers (such as llama.cpp) that don't expose the configured context size in the API response, you can manually specify the context size in `provider_options`:
 
-```yaml {5,6}
+```yaml {6,7}
 genai:
-  provider: openai
-  base_url: http://your-llama-server
-  model: your-model-name
-  provider_options:
-    context_size: 8192 # Specify the configured context size
+  my_provider:
+    provider: openai
+    base_url: http://your-llama-server
+    model: your-model-name
+    provider_options:
+      context_size: 8192 # Specify the configured context size
 ```
 
 This ensures Frigate uses the correct context window size when generating prompts.
@@ -377,11 +432,91 @@ To start using Azure OpenAI, you must first [create a resource](https://learn.mi
 
 ```yaml
 genai:
-  provider: azure_openai
-  base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
-  model: gpt-5-mini
-  api_key: "{FRIGATE_OPENAI_API_KEY}"
+  my_provider:
+    provider: azure_openai
+    base_url: https://instance.cognitiveservices.azure.com/openai/responses?api-version=2025-04-01-preview
+    model: gpt-5-mini
+    api_key: "{FRIGATE_OPENAI_API_KEY}"
 ```
 
 </TabItem>
 </ConfigTabs>
+
+## FAQ
+
+<FaqItem id="how-do-i-debug-genai-issues" question="How do I debug GenAI issues?">
+
+Frigate's Generative AI features are configured and enabled separately. [Review descriptions and summaries](/configuration/genai/genai_review) live under `review.genai`, and [object descriptions](/configuration/genai/genai_objects) live under `objects.genai`. Configuring a provider on this page does not enable either feature, and enabling one does not enable the other. Decide which of the two is not working, then work through the steps below.
+
+1. Confirm a provider is available and holds the `descriptions` role.
+   - Review descriptions, review summaries, and object descriptions all use the provider that has the `descriptions` role assigned in <NavPath path="Settings > Enrichments > Generative AI > Roles" /> (`genai.<provider>.roles`).
+   - A provider is contacted the first time one of its roles is actually used. A provider holding the `embeddings` role for semantic search is initialized during startup, while a `descriptions` provider is not initialized until the first description is requested, which may be well after boot.
+   - In <NavPath path="Settings > Enrichments > Generative AI" />, use **Refresh models** next to the model field. It queries the provider for its model list and is a quick way to verify that the base URL, API key, and network path between Frigate and your provider are correct.
+
+2. Confirm the feature you expect is actually enabled.
+   - Object descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Objects > GenAI object config > Enable GenAI" /> (`objects.genai.enabled`), either globally or per camera. This is the most common reason custom prompts appear to be ignored while review summaries are still being generated.
+   - Review descriptions are disabled by default. Turn on <NavPath path="Settings > Global configuration > Review > GenAI config > Enable GenAI descriptions" /> (`review.genai.enabled`). Once enabled, alerts are described by default but detections are not, so a detection-only review item will never get a summary unless **Enable GenAI for detections** (`review.genai.detections`) is also on.
+
+3. If object descriptions are never requested, check the filters that skip generation.
+   - <NavPath path="Settings > Global configuration > Objects > GenAI object config > GenAI objects" /> (`objects.genai.objects`) limits generation to specific labels, and **Required zones** (`objects.genai.required_zones`) requires the object to have entered one of those zones. If either is set and does not match, Frigate skips the request silently.
+   - Thumbnails are only collected while an object is moving. Objects that go stationary early contribute fewer frames.
+   - **Use snapshots** (`objects.genai.use_snapshot`) requires snapshots to be enabled for the camera. If the snapshot cannot be read, Frigate logs `Cannot load snapshot for <id>, file not found` and no description is generated.
+   - **Send on end** (`objects.genai.send_triggers.tracked_object_end`) is on by default. If you have turned it off in favor of **Early GenAI trigger** (`objects.genai.send_triggers.after_significant_updates`), descriptions are only requested once that number of updates is reached.
+
+4. Enable debug logs to see exactly what Frigate is doing. Restart Frigate after this change. The next step also requires a restart, so turn both on at the same time to avoid restarting twice.
+
+   ```yaml
+   logger:
+     default: info
+     logs:
+       # highlight-start
+       frigate.genai: debug
+       frigate.data_processing.post.object_descriptions: debug
+       frigate.data_processing.post.review_descriptions: debug
+       # highlight-end
+   ```
+
+5. Save the exact images and prompts that were sent to your provider.
+   - Turn on **Save thumbnails** for the feature you are debugging (`review.genai.debug_save_thumbnails` or `objects.genai.debug_save_thumbnails`). Both features write to `/media/frigate/clips/genai-requests/`, and these files are admin-only.
+   - Review descriptions write `genai-requests/<review_id>/` containing the numbered frames that were sent, plus `prompt.txt` and `response.txt` with the exact prompt and the raw, unparsed model response.
+   - Review summary reports write `genai-requests/<start_ts>-<end_ts>/prompt.txt` and `response.txt`. No images are involved, since a report summarizes existing review descriptions.
+   - Object descriptions write `genai-requests/<event_id>/` containing the numbered thumbnails. The prompt for object descriptions is not written to a file, it is only visible in the debug logs from step 4.
+   - Look at the saved images before blaming the model. If the object is small, blurry, or out of frame, no prompt will fix the result. For object descriptions, consider turning on **Use snapshots** (`objects.genai.use_snapshot`) to send a higher quality image. For review items, consider setting **Review image source** (`review.genai.image_source`) to `recordings` for 480p frames instead of the lower resolution preview frames.
+
+<ConfigTabs>
+<TabItem value="ui">
+
+For review descriptions, navigate to <NavPath path="Settings > Global configuration > Review" /> and set **GenAI config > Save thumbnails** to on.
+
+For object descriptions, navigate to <NavPath path="Settings > Global configuration > Objects" />, expand **GenAI object config**, and set **Save thumbnails** to on.
+
+</TabItem>
+<TabItem value="yaml">
+
+```yaml
+review:
+  genai:
+    enabled: true
+    # highlight-next-line
+    debug_save_thumbnails: true
+
+objects:
+  genai:
+    enabled: true
+    # highlight-next-line
+    debug_save_thumbnails: true
+```
+
+</TabItem>
+</ConfigTabs>
+
+6. Verify the prompt is what you think it is.
+   - Object description prompts are the ones you control directly. A camera-level <NavPath path="Settings > Camera configuration > Objects > GenAI object config > Caption prompt" /> (`objects.genai.prompt`) overrides the global one, and an entry in **Object prompts** (`objects.genai.object_prompts`) for a label overrides both for that label. Only `{label}`, `{sub_label}`, and `{camera}` are substituted.
+   - Review description prompts are built by Frigate and request a structured JSON response, so they are not fully replaceable. The parts you control are <NavPath path="Settings > Global configuration > Review > GenAI config > Activity context prompt" /> (`review.genai.activity_context_prompt`) and **Additional concerns** (`review.genai.additional_concerns`). Keep the activity context prompt general, since overly specific rules will sway the model's threat level scoring.
+
+7. If descriptions are generated but the results are poor or inconsistent, look at the model and the context window.
+   - Empty fields, missing `shortSummary` values, or `Failed to parse review description` errors usually mean the model is not following the requested JSON schema. Smaller models struggle with structured output. Try a larger parameter size or one of the [recommended models](#recommended-local-models).
+   - Frigate calculates how many frames to send from the context size the provider reports. If your server reports a different value than it is actually running with, frames will be truncated or the request will fail. Pin the value by adding `context_size` under <NavPath path="Settings > Enrichments > Generative AI > Provider options" /> (`genai.<provider>.provider_options`), and for Ollama also confirm `options.num_ctx` there matches the context you have configured.
+   - Check **Review Description Speed** and **Object Description Speed** in <NavPath path="System metrics > Enrichments" />. If inference takes tens of seconds, requests will queue behind each other and descriptions will appear to stop. For Ollama, review `OLLAMA_NUM_PARALLEL`, `OLLAMA_MAX_QUEUE`, and `OLLAMA_MAX_LOADED_MODELS` so that concurrent requests from Frigate are handled the way you expect.
+
+</FaqItem>

docs/docs/configuration/genai/objects.md

@@ -52,9 +52,10 @@ You can define custom prompts at the global level and per-object type. To config
 
 ```yaml
 genai:
-  provider: ollama
-  base_url: http://localhost:11434
-  model: qwen3-vl:8b-instruct
+  my_provider:
+    provider: ollama
+    base_url: http://localhost:11434
+    model: qwen3-vl:8b-instruct
 
 objects:
   genai:
@@ -112,3 +113,7 @@ Many providers also have a public facing chat interface for their models. Downlo
 - OpenAI - [ChatGPT](https://chatgpt.com)
 - Gemini - [Google AI Studio](https://aistudio.google.com)
 - Ollama - [Open WebUI](https://docs.openwebui.com/)
+
+## Troubleshooting
+
+If descriptions are not being generated, or the generated descriptions are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).

docs/docs/configuration/genai/review_summaries.md

@@ -201,3 +201,7 @@ Along with individual review item summaries, Generative AI can also produce a si
 Review reports can be requested via the [API](/integrations/api/generate-review-summary-review-summarize-start-start-ts-end-end-ts-post) by sending a POST request to `/api/review/summarize/start/{start_ts}/end/{end_ts}` with Unix timestamps.
 
 For Home Assistant users, there is a built-in service (`frigate.review_summarize`) that makes it easy to request review reports as part of automations or scripts. This allows you to automatically generate daily summaries, vacation reports, or custom time period reports based on your specific needs.
+
+## Troubleshooting
+
+If summaries are not being generated, or the generated summaries are not what you expect, see [How do I debug GenAI issues?](/configuration/genai/genai_config#how-do-i-debug-genai-issues).

docs/docs/configuration/go2rtc.md

@@ -15,7 +15,7 @@ Frigate uses the bundled go2rtc to power a number of key features:
 
 :::tip[Most users no longer need to configure go2rtc by hand]
 
-The **camera setup wizard** is the recommended way to add cameras. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />, and the wizard probes your camera and writes its configuration for you, including the go2rtc restream and the live stream mapping, so go2rtc is set up automatically.
+The [**camera setup wizard**](cameras.md#adding-a-camera-with-the-add-camera-wizard) is the recommended way to add cameras. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />, and the wizard probes your camera and writes its configuration for you, including the go2rtc restream and the live stream mapping, so go2rtc is set up automatically.
 
 This guide is mainly useful if you are **upgrading from an older version and have existing cameras that don't yet use go2rtc**, or if you want to fine-tune a stream by hand (for example, to transcode a codec your browser can't play). The [go2rtc troubleshooting guide](/troubleshooting/go2rtc) applies regardless of how your cameras were added.
 
@@ -67,4 +67,6 @@ If your stream won't play, has no audio, uses excessive CPU, or otherwise misbeh
 
 ## Homekit Configuration
 
-To add camera streams to Homekit Frigate must be configured in docker to use `host` networking mode. Once that is done, you can use the go2rtc WebUI (accessed via port 1984, which is disabled by default) to export a camera to Homekit. Any changes made will automatically be saved to `/config/go2rtc_homekit.yml`.
+To export camera streams to HomeKit, Frigate must be configured in docker to use `host` networking mode. HomeKit settings are stored in `/config/go2rtc_homekit.yml` rather than in your Frigate config, and are edited through the go2rtc config editor at `http://<frigate_host>:1984/editor.html`. Pairings are saved back to that file automatically.
+
+See the [HomeKit integration docs](/integrations/homekit) for the full setup, including the video and audio requirements HomeKit places on the stream.

docs/docs/configuration/license_plate_recognition.md

@@ -8,7 +8,7 @@ import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
 import FaqItem from "@site/src/components/FaqItem";
 
-Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car` or `motorcycle`. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
+Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a [known](#matching) name as a `sub_label` to tracked objects of type `car`, `motorcycle`, `bus`, `truck`, `school_bus`, or `garbage_truck`, depending on which of those labels your model detects. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.
 
 LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. When a vehicle becomes stationary, LPR continues to run for a short time after to attempt recognition.
 
@@ -24,7 +24,7 @@ When a plate is recognized, the details are:
 - Viewable in the Details pane in Review/History.
 - Viewable in the Tracked Object Details pane in Explore (sub labels and recognized license plates).
 - Filterable through the More Filters menu in Explore.
-- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the `car` or `motorcycle` tracked object.
+- Published via the `frigate/events` MQTT topic as a `sub_label` ([known](#matching)) or `recognized_license_plate` (unknown) for the vehicle tracked object.
 - Published via the `frigate/tracked_object_update` MQTT topic with `name` (if [known](#matching)) and `plate`.
 
 ## Model Requirements
@@ -35,7 +35,7 @@ Users without a model that detects license plates can still run LPR. Frigate use
 
 :::note
 
-In the default mode, Frigate's LPR needs to first detect a `car` or `motorcycle` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` or `motorcycle` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
+In the default mode, Frigate's LPR needs to first detect a vehicle before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a vehicle will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
 
 :::
 
@@ -86,7 +86,7 @@ cameras:
 </TabItem>
 </ConfigTabs>
 
-For non-dedicated LPR cameras, ensure that your camera is configured to detect objects of type `car` or `motorcycle`, and that a car or motorcycle is actually being detected by Frigate. Otherwise, LPR will not run.
+For non-dedicated LPR cameras, ensure that your camera is configured to detect vehicle objects, and that a vehicle is actually being detected by Frigate. Otherwise, LPR will not run. The object types that can carry a plate are defined by your model's `attributes_map`, so if your model detects other vehicle labels, you can add them there.
 
 Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the `detect` role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.
 
@@ -158,7 +158,7 @@ lpr:
 
 Navigate to <NavPath path="Settings > Enrichments > License plate recognition" />.
 
-- **Known plates**: Assign custom `sub_label` values to `car` and `motorcycle` objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
+- **Known plates**: Assign custom `sub_label` values to vehicle objects when a recognized plate matches a known value. These labels appear in the UI, filters, and notifications. Unknown plates are still saved but are added to the `recognized_license_plate` field rather than the `sub_label`.
 - **Match distance**: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate. For example, setting to `1` allows a plate `ABCDE` to match `ABCBE` or `ABCD`. This parameter will _not_ operate on known plates that are defined as regular expressions.
 
 </TabItem>
@@ -316,7 +316,7 @@ lpr:
 
 :::note
 
-If a camera is configured to detect `car` or `motorcycle` but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
+If a camera is configured to detect vehicles but you don't want Frigate to run LPR for that camera, disable LPR at the camera level:
 
 <ConfigTabs>
 <TabItem value="ui">
@@ -456,7 +456,7 @@ With this setup:
 - Snapshots will have license plate bounding boxes on them.
 - The `frigate/events` MQTT topic will publish tracked object updates.
 - Debug view will display `license_plate` bounding boxes.
-- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the `car` / `motorcycle` and the `license_plate` in the snapshots on the Frigate+ website, even if the car is barely visible.
+- If you are using a Frigate+ model and want to submit images from your dedicated LPR camera for model training and fine-tuning, annotate both the vehicle and the `license_plate` in the snapshots on the Frigate+ website, even if the vehicle is barely visible.
 
 ### Using the Secondary LPR Pipeline (Without Frigate+)
 
@@ -611,9 +611,9 @@ If you are still having issues detecting plates, start with a basic configuratio
 
 </FaqItem>
 
-<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting <code>car</code> or <code>motorcycle</code> objects?</>}>
+<FaqItem id="can-i-run-lpr-without-detecting-car-or-motorcycle-objects" question={<>Can I run LPR without detecting vehicle objects?</>}>
 
-In normal LPR mode, Frigate requires a `car` or `motorcycle` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
+In normal LPR mode, Frigate requires a vehicle to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
 
 </FaqItem>
 
@@ -699,7 +699,7 @@ lpr:
 4. Ensure the characters on detected plates are being _recognized_.
    - Check the **Plate recognition** inference time in Enrichment metrics (<NavPath path="System metrics > Enrichments" />). High inference times (> 100ms) could lead to poor recognition results, especially for dedicated LPR cameras where the plate crosses the frame quickly.
    - Enable `debug_save_plates` to save images of detected text on plates to the clips directory (`/media/frigate/clips/lpr`). Ensure these images are readable and the text is clear.
-   - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the `car` or `motorcycle` label will change to the recognized plate when LPR is enabled and working.
+   - Watch the debug view to see plates recognized in real-time. For non-dedicated LPR cameras, the vehicle's label will change to the recognized plate when LPR is enabled and working.
    - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 
 </FaqItem>
@@ -714,13 +714,13 @@ LPR's performance impact depends on your hardware. Ensure you have at least 4GB
 
 The YOLOv9 license plate detector model will run (and the metric will appear) if you've enabled LPR but haven't defined `license_plate` as an object to track, either at the global or camera level.
 
-If you are detecting `car` or `motorcycle` on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
+If you are detecting vehicles on cameras where you don't want to run LPR, make sure you disable LPR it at the camera level. And if you do want to run LPR on those cameras, make sure you define `license_plate` as an object to track.
 
 </FaqItem>
 
 <FaqItem id="it-looks-like-frigate-picked-up-my-cameras-timestamp-or-overlay-text-as-the-license-plate-how-can-i-prevent-this" question="It looks like Frigate picked up my camera's timestamp or overlay text as the license plate. How can I prevent this?">
 
-This could happen if cars or motorcycles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
+This could happen if vehicles travel close to your camera's timestamp or overlay text. You could either move the text through your camera's firmware, or apply a mask to it in Frigate.
 
 If you are using a model that natively detects `license_plate`, add an _object mask_ of type `license_plate` and a _motion mask_ over your text.
 

docs/docs/configuration/live.md

@@ -6,6 +6,7 @@ title: Live View
 import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
 
 Frigate intelligently displays your camera streams on the Live view dashboard. By default, Frigate employs "smart streaming" where camera images update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any motion or active objects are detected, cameras seamlessly switch to a live stream.
 
@@ -33,7 +34,7 @@ If you are using go2rtc, you should adjust the following settings in your camera
 
 - Video codec: **H.264** - provides the most compatible video codec with all Live view technologies and browsers. Avoid any kind of "smart codec" or "+" codec like _H.264+_ or _H.265+_. as these non-standard codecs remove keyframes (see below).
 - Audio codec: **AAC** - provides the most compatible audio codec with all Live view technologies and browsers that support audio.
-- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
+- I-frame interval (sometimes called the keyframe interval, the interframe space, or the GOP length): match your camera's frame rate, or choose "1x" (for interframe space on Reolink cameras). For example, if your stream outputs 20fps, your i-frame interval should be 20 (or 1x on Reolink). Values higher than the frame rate will cause the stream to take longer to begin playback. See [this page](https://web.archive.org/web/20251213190836/https://gardinal.net/understanding-the-keyframe-interval/) for more on keyframes. For many users this may not be an issue, but it should be noted that a 1x i-frame interval will cause more storage utilization if you are using the stream for the `record` role as well.
 
 The default video and audio codec on your camera may not always be compatible with your browser, which is why setting them to H.264 and AAC is recommended. See the [go2rtc docs](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#codecs-madness) for codec support information.
 
@@ -195,7 +196,7 @@ services:
 
 :::
 
-See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.8.3#module-webrtc) for more information about this.
+See [go2rtc WebRTC docs](https://github.com/AlexxIT/go2rtc/tree/v1.9.14#module-webrtc) for more information about this.
 
 ### Two way talk
 
@@ -333,7 +334,7 @@ When your browser runs into problems playing back your camera streams, it will l
 
 - **stalled**
   - What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
-  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in the UI pane of Frigate's settings.
+  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in <NavPath path="Settings > UI" /> .
 
   - Possible console messages from the player code:
     - `Buffer time (10 seconds) exceeded, browser may not be playing media correctly.`
@@ -341,100 +342,155 @@ When your browser runs into problems playing back your camera streams, it will l
 
 ## Live view FAQ
 
-1. **Why don't I have audio in my Live view?**
+### Getting Live View Working
 
-   You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
+<FaqItem id="why-dont-i-have-audio-in-my-live-view" question="Why don't I have audio in my Live view?">
 
-   Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
+You must use go2rtc to hear audio in your live streams. If you have go2rtc already configured, you need to ensure your camera is sending PCMA/PCMU or AAC audio. If you can't change your camera's audio codec, you need to [transcode the audio](https://github.com/AlexxIT/go2rtc?tab=readme-ov-file#source-ffmpeg) using go2rtc.
 
-2. **Frigate shows that my live stream is in "low bandwidth mode". What does this mean?**
+If the audio controls don't appear in the UI at all, verify that the Live view is actually using your go2rtc stream. If your go2rtc stream names don't match your Frigate camera name, you must map them with the `live -> streams` config (see [Setting Streams For Live UI](#setting-streams-for-live-ui) above); otherwise the UI falls back to the video-only jsmpeg player.
 
-   Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
+Note that the low bandwidth mode player is a video-only stream. You should not expect to hear audio when in low bandwidth mode, even if you've set up go2rtc.
 
-   When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. Continuous streaming mode does not have an automatic reset mechanism, but you can use the _Reset_ option to force a reload of your stream.
+</FaqItem>
 
-   If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
+<FaqItem id="i-have-unmuted-some-cameras-on-my-dashboard-but-i-do-not-hear-sound-why" question="I have unmuted some cameras on my dashboard, but I do not hear sound. Why?">
 
-   Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
-   - Network issues (e.g., MSE or WebRTC network connection problems).
-   - Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
-   - Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
-   - Browser compatibility problems (e.g., iOS Safari limitations with MSE).
+If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
 
-   To view browser console logs:
-   1. Open the Frigate Live View in your browser.
-   2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
-   3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
-   4. Look for messages prefixed with the camera name.
+</FaqItem>
 
-   These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
-   - Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera-settings-recommendations)).
-   - Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
-   - Test with a different stream via the UI dropdown (if `live -> streams` is configured).
-   - For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see [WebRTC Extra Configuration](#webrtc-extra-configuration)).
-   - If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
+<FaqItem id="my-live-view-shows-a-black-screen-or-doesnt-load-but-the-debug-view-works-why" question="My live view shows a black screen or doesn't load, but the debug view works. Why?">
 
-3. **It doesn't seem like my cameras are streaming on the Live dashboard. Why?**
+The debug view plays the `detect` stream processed by Frigate itself, while the Live view plays your go2rtc stream directly in the browser. If the debug view works but the Live view doesn't, your browser usually can't decode what the camera is sending, most often H.265 video or an incompatible audio track.
 
-   On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
+Work through the [go2rtc troubleshooting guide](/troubleshooting/go2rtc#live-view-is-black-buffering-or-stuck-in-low-bandwidth-mode) to isolate the problem. Two fixes resolve the majority of cases:
 
-4. **I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?**
+1. Restream through go2rtc's FFmpeg module by prefixing your source with `ffmpeg:`, for example `- ffmpeg:rtsp://user:password@192.168.1.5:554/stream`.
+2. If that doesn't help, transcode to compatible codecs: `- ffmpeg:rtsp://user:password@192.168.1.5:554/stream#video=h264#audio=aac#hardware`.
 
-   This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
+</FaqItem>
 
-5. **How does "smart streaming" work?**
+<FaqItem id="how-do-i-get-the-best-live-view-experience-in-home-assistant" question="How do I get the best live view experience in Home Assistant?">
 
-   Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
+For a full-resolution, low-latency live view in Home Assistant dashboards, use the [Advanced Camera Card](https://card.camera) with the [go2rtc live provider](https://card.camera/#/configuration/cameras/live-provider?id=go2rtc), which streams directly from Frigate's bundled go2rtc. This also supports audio and [two-way talk](#two-way-talk) on capable cameras. See the [Home Assistant integration docs](/integrations/home-assistant) for setup.
 
-   This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
+</FaqItem>
 
-   Smart streaming depends on having your camera's motion `threshold` and `contour_area` config values dialed in. Use the Motion Tuner in Settings in the UI to tune these values in real-time.
+### Streaming Behavior
 
-   This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
+<FaqItem id="how-does-smart-streaming-work" question={'How does "smart streaming" work?'}>
 
-6. **I have unmuted some cameras on my dashboard, but I do not hear sound. Why?**
+Because a static image of a scene looks exactly the same as a live stream with no motion or activity, smart streaming updates your camera images once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity (motion or object/audio detection) occurs, cameras seamlessly switch to a live stream.
 
-   If your camera is streaming (as indicated by a red dot in the upper right, or if it has been set to continuous streaming mode), your browser may be blocking audio until you interact with the page. This is an intentional browser limitation. See [this article](https://developer.mozilla.org/en-US/docs/Web/Media/Autoplay_guide#autoplay_availability). Many browsers have a whitelist feature to change this behavior.
+This static image is pulled from the stream defined in your config with the `detect` role. When activity is detected, images from the `detect` stream immediately begin updating at ~5 frames per second so you can see the activity until the live player is loaded and begins playing. This usually only takes a second or two. If the live player times out, buffers, or has streaming errors, the jsmpeg player is loaded and plays a video-only stream from the `detect` role. When activity ends, the players are destroyed and a static image is displayed until activity is detected again, and the process repeats.
 
-7. **My camera streams have lots of visual artifacts / distortion.**
+Smart streaming depends on having your camera's motion `threshold` and `contour_area` config values dialed in. Use the Motion Tuner in Settings in the UI to tune these values in real-time.
 
-   Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
+This is Frigate's default and recommended setting because it results in a significant bandwidth savings, especially for high resolution cameras.
 
-8. **Why does my camera stream switch aspect ratios on the Live dashboard?**
+</FaqItem>
 
-   Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
+<FaqItem id="it-doesnt-seem-like-my-cameras-are-streaming-on-the-live-dashboard-why" question="It doesn't seem like my cameras are streaming on the Live dashboard. Why?">
 
-   To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
+On the default Live dashboard ("All Cameras"), your camera images will update once per minute when no detectable activity is occurring to conserve bandwidth and resources. As soon as any activity is detected, cameras seamlessly switch to a full-resolution live stream. If you want to customize this behavior, use a camera group.
 
-   Example: Resolutions from two streams
-   - Mismatched (may cause aspect ratio switching on the dashboard):
-     - Live/go2rtc stream: 1920x1080 (16:9)
-     - Detect stream: 640x352 (~1.82:1, not 16:9)
+</FaqItem>
 
-   - Matched (prevents switching):
-     - Live/go2rtc stream: 1920x1080 (16:9)
-     - Detect stream: 640x360 (16:9)
+<FaqItem id="frigate-shows-that-my-live-stream-is-in-low-bandwidth-mode-what-does-this-mean" question={'Frigate shows that my live stream is in "low bandwidth mode". What does this mean?'}>
 
-   You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
+Frigate intelligently selects the live streaming technology based on a number of factors (user-selected modes like two-way talk, camera settings, browser capabilities, available bandwidth) and prioritizes showing an actual up-to-date live view of your camera's stream as quickly as possible.
 
-   ```yaml
-   cameras:
-     front_door:
-       detect:
-         width: 640
-         height: 360 # set this to 360 instead of 352
-       ffmpeg:
-         inputs:
-           - path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
-             roles:
-               - record
-           - path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
-             roles:
-               - detect
-   ```
+When you have go2rtc configured, Live view initially attempts to load and play back your stream with a clearer, fluent stream technology (MSE). An initial timeout, a low bandwidth condition that would cause buffering of the stream, or decoding errors in the stream will cause Frigate to switch to the stream defined by the `detect` role, using the jsmpeg format. This is what the UI labels as "low bandwidth mode". On Live dashboards, the mode will automatically reset when smart streaming is configured and activity stops. Continuous streaming mode does not have an automatic reset mechanism, but you can use the _Reset_ option to force a reload of your stream.
 
-   The same applies to your `record` stream: if its aspect ratio differs from your `detect` stream, your recordings will appear in a different shape than the live view. For consistent framing across live view and recordings, use the same aspect ratio for all of a camera's streams (the resolution can still differ).
+If you are using continuous streaming or you are loading more than a few high resolution streams at once on the dashboard, your browser may struggle to begin playback of your streams before the timeout. Frigate always prioritizes showing a live stream as quickly as possible, even if it is a lower quality jsmpeg stream. You can use the "Reset" link/button to try loading your high resolution stream again.
 
-9. **Why does Frigate prefer MSE over WebRTC for live view?**
+Errors in stream playback (e.g., connection failures, codec issues, or buffering timeouts) that cause the fallback to low bandwidth mode (jsmpeg) are logged to the browser console for easier debugging. These errors may include:
 
-   Frigate prefers MSE because it delivers a better out-of-the-box experience than WebRTC on nearly every axis that matters for a security camera system. MSE is an open standard optimized and supported by all modern browsers, works without any extra configuration (WebRTC requires port forwarding and candidate setup, and lacks H.265 support in some browsers), and requires no internet access for NAT traversal. More importantly, MSE runs over TCP, so every frame arrives and is decoded in order, so nothing is ever silently skipped. WebRTC optimizes for latency over UDP by discarding late or incomplete frames, which works against you on cellular or spotty Wi-Fi: you can end up with frozen video, visual corruption, or gaps in the feed without ever knowing you missed something. Frigate's enhanced MSE player has adaptive speed playback and has been tuned for latency and connection robustness that meets or exceeds WebRTC, so you get near-real-time playback with a guarantee that when the video plays, every frame is actually there - which, for an NVR whose whole purpose is letting you see what happened, matters more than shaving fractions of a second off a latency number. That's why Frigate defaults to MSE and reserves WebRTC for cases that require it, like two-way talk.
+- Network issues (e.g., MSE or WebRTC network connection problems).
+- Unsupported codecs or stream formats (e.g., H.265 in WebRTC, which is not supported in some browsers).
+- Buffering timeouts or low bandwidth conditions causing fallback to jsmpeg.
+- Browser compatibility problems (e.g., iOS Safari limitations with MSE).
+
+To view browser console logs:
+
+1. Open the Frigate Live View in your browser.
+2. Open the browser's Developer Tools (F12 or right-click > Inspect > Console tab).
+3. Reproduce the error (e.g., load a problematic stream or simulate network issues).
+4. Look for messages prefixed with the camera name.
+
+These logs help identify if the issue is player-specific (MSE vs. WebRTC) or related to camera configuration (e.g., go2rtc streams, codecs). If you see frequent errors:
+
+- Verify your camera's H.264/AAC settings (see [Frigate's camera settings recommendations](#camera-settings-recommendations)).
+- Check go2rtc configuration for transcoding (e.g., audio to AAC/OPUS).
+- Test with a different stream via the UI dropdown (if `live -> streams` is configured).
+- For WebRTC-specific issues, ensure port 8555 is forwarded and candidates are set (see [WebRTC Extra Configuration](#webrtc-extra-configuration)).
+- If your cameras are streaming at a high resolution, your browser may be struggling to load all of the streams before the buffering timeout occurs. Frigate prioritizes showing a true live view as quickly as possible. If the fallback occurs often, change your live view settings to use a lower bandwidth substream.
+
+</FaqItem>
+
+<FaqItem id="why-is-my-live-view-delayed-or-lagging-behind-real-time" question="Why is my live view delayed or lagging behind real time?">
+
+A delay when a stream first starts is usually caused by your camera's I-frame (keyframe) interval. Playback cannot begin until a keyframe arrives, so an interval set higher than your camera's frame rate makes the stream take longer to start. Set the I-frame interval to match the frame rate (or "1x" on Reolink) per the [camera settings recommendations](#camera-settings-recommendations).
+
+A stream that starts on time but falls further behind live is buffering, which is usually the browser struggling to decode too many high-resolution streams at once. Select a lower-bandwidth substream for your dashboards (see [Setting Streams For Live UI](#setting-streams-for-live-ui)), reduce the number of streams open at once, or improve the network connection between your browser and Frigate. Frigate's player automatically speeds up playback to catch up to live after buffering, and falls back to low bandwidth mode if it stalls for too long. The _Reset_ option forces a fresh connection at the live edge.
+
+</FaqItem>
+
+<FaqItem id="why-does-frigate-prefer-mse-over-webrtc-for-live-view" question="Why does Frigate prefer MSE over WebRTC for live view?">
+
+Frigate prefers MSE because it delivers a better out-of-the-box experience than WebRTC on nearly every axis that matters for a security camera system. MSE is an open standard optimized and supported by all modern browsers, works without any extra configuration (WebRTC requires port forwarding and candidate setup, and lacks H.265 support in some browsers), and requires no internet access for NAT traversal. More importantly, MSE runs over TCP, so every frame arrives and is decoded in order, so nothing is ever silently skipped. WebRTC optimizes for latency over UDP by discarding late or incomplete frames, which works against you on cellular or spotty Wi-Fi: you can end up with frozen video, visual corruption, or gaps in the feed without ever knowing you missed something. Frigate's enhanced MSE player has adaptive speed playback and has been tuned for latency and connection robustness that meets or exceeds WebRTC, so you get near-real-time playback with a guarantee that when the video plays, every frame is actually there - which, for an NVR whose whole purpose is letting you see what happened, matters more than shaving fractions of a second off a latency number. That's why Frigate defaults to MSE and reserves WebRTC for cases that require it, like two-way talk.
+
+</FaqItem>
+
+### Video Quality Issues
+
+<FaqItem id="i-see-a-strange-diagonal-line-on-my-live-view-but-my-recordings-look-fine-how-can-i-fix-it" question="I see a strange diagonal line on my live view, but my recordings look fine. How can I fix it?">
+
+This is caused by incorrect dimensions set in your detect width or height (or incorrectly auto-detected), causing the jsmpeg player's rendering engine to display a slightly distorted image. You should enlarge the width and height of your `detect` resolution up to a standard aspect ratio (example: 640x352 becomes 640x360, and 800x443 becomes 800x450, 2688x1520 becomes 2688x1512, etc). If changing the resolution to match a standard (4:3, 16:9, or 32:9, etc) aspect ratio does not solve the issue, you can enable "compatibility mode" in your camera group dashboard's stream settings. Depending on your browser and device, more than a few cameras in compatibility mode may not be supported, so only use this option if changing your `detect` width and height fails to resolve the color artifacts and diagonal line.
+
+</FaqItem>
+
+<FaqItem id="my-camera-streams-have-lots-of-visual-artifacts-or-distortion" question="My camera streams have lots of visual artifacts / distortion.">
+
+Some cameras don't include the hardware to support multiple connections to the high resolution stream, and this can cause unexpected behavior. In this case it is recommended to [restream](./restream.md) the high resolution stream so that it can be used for live view and recordings.
+
+</FaqItem>
+
+<FaqItem id="why-does-my-camera-stream-switch-aspect-ratios-on-the-live-dashboard" question="Why does my camera stream switch aspect ratios on the Live dashboard?">
+
+Your camera may change aspect ratios on the dashboard because Frigate uses different streams for different purposes. With go2rtc and Smart Streaming, Frigate shows a static image from the `detect` stream when no activity is present, and switches to the live stream when motion is detected. The camera image will change size if your streams use different aspect ratios.
+
+To prevent this, make the `detect` stream match the go2rtc live stream's aspect ratio (resolution does not need to match, just the aspect ratio). You can either adjust the camera's output resolution or set the `width` and `height` values in your config's `detect` section to a resolution with an aspect ratio that matches.
+
+Example: Resolutions from two streams
+
+- Mismatched (may cause aspect ratio switching on the dashboard):
+  - Live/go2rtc stream: 1920x1080 (16:9)
+  - Detect stream: 640x352 (~1.82:1, not 16:9)
+
+- Matched (prevents switching):
+  - Live/go2rtc stream: 1920x1080 (16:9)
+  - Detect stream: 640x360 (16:9)
+
+You can update the detect settings in your camera config to match the aspect ratio of your go2rtc live stream. For example:
+
+```yaml
+cameras:
+  front_door:
+    detect:
+      width: 640
+      height: 360 # set this to 360 instead of 352
+    ffmpeg:
+      inputs:
+        - path: rtsp://127.0.0.1:8554/front_door # main stream 1920x1080
+          roles:
+            - record
+        - path: rtsp://127.0.0.1:8554/front_door_sub # sub stream 640x352
+          roles:
+            - detect
+```
+
+The same applies to your `record` stream: if its aspect ratio differs from your `detect` stream, your recordings will appear in a different shape than the live view. For consistent framing across live view and recordings, use the same aspect ratio for all of a camera's streams (the resolution can still differ).
+
+</FaqItem>

docs/docs/configuration/notifications.md

@@ -6,6 +6,7 @@ title: Notifications
 import ConfigTabs from "@site/src/components/ConfigTabs";
 import TabItem from "@theme/TabItem";
 import NavPath from "@site/src/components/NavPath";
+import FaqItem from "@site/src/components/FaqItem";
 
 # Notifications
 
@@ -21,7 +22,7 @@ Push notifications require internet access from the Frigate server to the browse
 
 In order to use notifications the following requirements must be met:
 
-- Frigate must be accessed via a secure `https` connection ([see the authorization docs](/configuration/authentication)).
+- Frigate must be accessed via a secure `https` connection while signed in as a Frigate user ([see the authorization docs](/configuration/authentication)).
 - A supported browser must be used. Currently Chrome, Firefox, and Safari are known to be supported.
 - In order for notifications to be usable externally, Frigate must be accessible externally.
 - For iOS devices, some users have also indicated that the Notifications switch needs to be enabled in iOS Settings --> Apps --> Safari --> Advanced --> Features.
@@ -85,7 +86,13 @@ cameras:
 
 ### Registration
 
-Once notifications are enabled, press the `Register for Notifications` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
+Once notifications are enabled, press the `Register This Device` button on all devices that you would like to receive notifications on. This will register the background worker. After this Frigate must be restarted and then notifications will begin to be sent.
+
+:::warning
+
+Each registration is attached to the Frigate user account you are signed in as, so you must register over a secure connection to the authenticated port (`8971`). Reverse proxies and tunnels should point at port `8971`.
+
+:::
 
 ## Supported Notifications
 
@@ -104,3 +111,62 @@ Different platforms handle notifications differently, some settings changes may
 ### Android
 
 Most Android phones have battery optimization settings. To get reliable Notification delivery the browser (Chrome, Firefox) should have battery optimizations disabled. If Frigate is running as a PWA then the Frigate app should have battery optimizations disabled as well.
+
+## Notifications FAQ
+
+<FaqItem id="how-do-i-debug-notifications-issues" question="How do I debug notifications issues?">
+
+Push notifications involve Frigate, your browser, and your browser vendor's push service, so it helps to work from the server outward.
+
+1. Enable debug logs for the push client by adding `frigate.comms.webpush: debug` to your `logger` configuration. Restart Frigate after this change.
+
+   ```yaml
+   logger:
+     default: info
+     logs:
+       # highlight-next-line
+       frigate.comms.webpush: debug
+   ```
+
+   These logs show exactly where a notification stopped, including:
+   - `Email must be provided for push notifications to be sent` means the global `email` field is empty and nothing will ever be sent.
+   - `Sending test notification` and `Sending push notification for <camera>, review ID <id>` mean Frigate handed the message off to the push service.
+   - `Skipping notification for <camera> - in global cooldown period` (or `camera-specific cooldown period`) means your [cooldown](#configuration) values suppressed it.
+   - `Notifications for <camera> are currently suspended` means notifications were suspended from <NavPath path="Settings > Notifications" /> or MQTT.
+   - `Notification endpoint expired for <user>, received 410` means that device's subscription is no longer valid and it must be re-registered.
+   - `Failed to send notification to <user> :: <status>` means the push service rejected the message. A `401` or `403` usually points at a VAPID or `email` problem, and a `5xx` is a problem on the push service's end.
+   - If you see no messages at all when an alert occurs, the notification was never queued. Confirm an actual **alert** was created (notifications are not sent for detections), and that notifications are enabled both globally and for that camera.
+
+2. Verify the basics that most reports come down to:
+   - Frigate must be reached over `https` with a certificate your device trusts. Browsers silently refuse to register a service worker otherwise, and a self-signed certificate that is not installed as trusted on the device will fail.
+   - On iOS, notifications only work when Frigate has been installed to the Home Screen via **Share > Add to Home Screen** and opened from that icon. Safari and Chrome tabs cannot receive web push on iOS.
+   - Each device must be registered individually, and Frigate must be restarted after registering before anything can be sent, including test notifications.
+   - The Frigate server needs outbound internet access to the browser vendor's push service. See [Network Requirements](/frigate/network_requirements#push-notifications).
+
+3. Test from the UI. Use the `Send a test notification` button in <NavPath path="Settings > Notifications" />. If the log shows `Sending test notification` but nothing arrives on the device, the problem is between the push service and your device rather than in Frigate.
+
+4. Check the browser side on the device that is not receiving notifications:
+   - Confirm the site's notification permission is set to **Allow** in your browser or OS settings, and that a focus/do not disturb mode is not hiding them.
+   - In desktop browsers, open Developer Tools > Application > Service Workers and confirm `notifications-worker.js` is registered and activated. Unregistering it and registering the device again will rebuild a broken subscription.
+   - Check the browser console and your reverse proxy logs for failures loading `/notifications-worker.js` or errors on `/api/notifications/register`.
+
+</FaqItem>
+
+<FaqItem id="why-did-notifications-stop-arriving-after-working-for-a-while" question="Why did notifications stop arriving after working for a while?">
+
+Push subscriptions are issued by the browser vendor and can be revoked, most often after a browser update, after clearing site data, or when a device has been offline for an extended period. When this happens the device still appears registered in Frigate, but the push service rejects the message. The debug logs will show `Notification endpoint expired` with a `404` or `410` status.
+
+Unregister and re-register the affected device from <NavPath path="Settings > Notifications" />, then restart Frigate.
+
+</FaqItem>
+
+<FaqItem id="why-am-i-not-getting-notifications-for-one-specific-camera" question="Why am I not getting notifications for one specific camera?">
+
+Work through these in order:
+
+- Notifications are only sent for **alerts**. If the camera is producing detections instead, adjust the camera's `review > alerts > labels` so the objects you care about are classified as alerts.
+- Confirm notifications are enabled for that camera in <NavPath path="Settings > Camera configuration > Notifications" />.
+- Check the camera's `cooldown` value, and remember that the global cooldown applies across all cameras. A busy camera can consume the global cooldown and suppress a quieter one.
+- If [authentication](/configuration/authentication) is enabled with roles, users only receive notifications for the cameras their role grants access to.
+
+</FaqItem>

docs/docs/configuration/object_detectors.md

@@ -24,7 +24,6 @@ Frigate supports multiple different detectors that work on different types of ha
 - [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
 - [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
 - <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
-- <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
 
 **AMD**
 
@@ -298,6 +297,14 @@ detectors:
 
 :::
 
+### Intel NPU host requirements {#intel-npu-requirements}
+
+The NPU firmware is loaded by the host kernel and is not part of the Frigate image. Everything else the NPU needs is bundled in the container, so host NPU libraries should never be mounted in.
+
+Frigate bundles a specific version of Intel's [linux-npu-driver](https://github.com/intel/linux-npu-driver/releases), and the host firmware must come from that release or a newer one. Firmware older than the bundled driver may fail with `MAPPED_INFERENCE_VERSION is NOT compatible with the ELF`, where `Expected` is the version the firmware supports and `received` is the version the bundled compiler produced. Distributions often package older firmware than the driver Frigate ships, so check the build date on the host with `sudo dmesg | grep -i vpu` and update it there if needed.
+
+Intel NPUs cannot be used under Home Assistant OS, which does not include the NPU firmware.
+
 ### Configuration {#configuration-openvino}
 
 <ModelConfigDropdown detectorTitle="OpenVINO" models={objectDetectorsModels.openvino.models} />
@@ -755,87 +762,6 @@ Explanation of the parameters:
   - **example**: Specifying `output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}"` could result in a model called `frigate-i8-my_model-rk3588-v2.3.0.rknn`.
 - `config`: Configuration passed to `rknn-toolkit2` for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of [this manual](https://github.com/MarcA711/rknn-toolkit2/releases/download/v2.3.2/03_Rockchip_RKNPU_API_Reference_RKNN_Toolkit2_V2.3.2_EN.pdf).
 
-## DeGirum
-
-DeGirum is a detector that can use any type of hardware listed on [their website](https://hub.degirum.com). DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of `@local`. You can also connect directly to DeGirum's AI Hub to run inferences. **Please Note:** This detector _cannot_ be used for commercial purposes.
-
-### Configuration {#configuration-degirum}
-
-#### AI Server Inference
-
-Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your `docker-compose.yml` to get started:
-
-```yaml
-degirum_detector:
-  container_name: degirum
-  image: degirum/aiserver:latest
-  privileged: true
-  ports:
-    - "8778:8778"
-```
-
-All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to [DeGirum's docs site](https://docs.degirum.com/pysdk/runtimes-and-drivers) if you have any trouble.
-
-Once completed, configure the detector as follows:
-
-<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumAiServer.models} />
-
-Setting up a model in the `config.yml` is similar to setting up an AI server.
-You can set it to:
-
-- A model listed on the [AI Hub](https://hub.degirum.com), given that the correct zoo name is listed in your detector
-  - If this is what you choose to do, the correct model will be downloaded onto your machine before running.
-- A local directory acting as a zoo. See DeGirum's docs site [for more information](https://docs.degirum.com/pysdk/user-guide-pysdk/organizing-models#model-zoo-directory-structure).
-- A path to some model.json.
-
-```yaml
-model:
-  path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
-#### Local Inference
-
-It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:
-
-1. Ensuring that the frigate docker container has the runtime you want to use. So for instance, running `@local` for Hailo means making sure the container you're using has the Hailo runtime installed.
-2. To double check the runtime is detected by the DeGirum detector, make sure the `degirum sys-info` command properly shows whatever runtimes you mean to install.
-3. Create a DeGirum detector in your configuration.
-
-<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumLocal.models} />
-
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
-
-```yaml
-model:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
-#### AI Hub Cloud Inference
-
-If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,
-
-1. Sign up at [DeGirum's AI Hub](https://hub.degirum.com).
-2. Get an access token.
-3. Create a DeGirum detector in your configuration.
-
-<ModelConfigDropdown detectorTitle="DeGirum" models={objectDetectorsModels.degirumCloud.models} />
-
-Once `degirum_detector` is setup, you can choose a model through 'model' section in the `config.yml` file.
-
-```yaml
-model:
-  path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
-  width: 300 # width is in the model name as the first number in the "int"x"int" section
-  height: 300 # height is in the model name as the second number in the "int"x"int" section
-  input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here
-```
-
 ## AXERA
 
 Hardware accelerated object detection is supported on the following SoCs:

docs/docs/configuration/profiles.md

@@ -126,7 +126,7 @@ Only the fields you explicitly set in a profile override are applied. All other
 
 ## Activating Profiles
 
-Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), or the Home Assistant integration.
+Profiles can be activated and deactivated via the Frigate UI, [MQTT](/integrations/mqtt#frigateprofileset), the [HTTP API](../integrations/api/camera-set-camera-camera-name-set-feature-sub-command-put.api.mdx), or the Home Assistant integration.
 
 In the Frigate UI, open the Settings cog and select **Profiles** from the submenu to see all defined profiles. From there you can activate any profile or deactivate the current one. The active profile is indicated in the UI so you always know which profile is in effect.
 
@@ -232,6 +232,21 @@ No. Only one profile can be active at a time. Activating a new profile automatic
 
 When you delete a base zone or mask in the Frigate UI, any profile overrides for that entry are deleted automatically as part of the same operation. If you remove a base entry by editing your config file directly and leave a profile override behind, the config will fail validation at startup until the orphaned override is removed as well.
 
+### How do I make a YAML profile track no objects at all?
+
+Set the tracked object list explicitly to an empty list in the profile:
+
+```yaml
+cameras:
+  front_door:
+    profiles:
+      home:
+        objects:
+          track: []
+```
+
+Leaving the `objects` section empty (or omitting `track`) does not clear the list. Empty sections set no fields, so the profile inherits the full tracked object list from the base config, including anything set at the global level. The same applies to other lists, such as `audio.listen`.
+
 ### Why are some settings missing when I configure a profile override?
 
 Fields that require a Frigate restart to take effect cannot be overridden by profiles, since profiles are applied at runtime without restarting. Those fields are hidden when editing a profile override and can only be changed on the base configuration.

docs/docs/configuration/record.md

@@ -9,7 +9,7 @@ import NavPath from "@site/src/components/NavPath";
 
 Recordings can be enabled and are stored at `/media/frigate/recordings`. The folder structure for the recordings is `YYYY-MM-DD/HH/<camera_name>/MM.SS.mp4` in **UTC time**. These recordings are written directly from your camera stream without re-encoding. Each camera supports a configurable retention policy. Frigate chooses the largest matching retention value between the recording retention and the tracked object retention when determining if a recording should be removed.
 
-New recording segments are written from the camera stream to cache, they are only moved to disk if they match the setup recording retention policy.
+New recording segments are written from the camera stream to cache, they are only moved to disk if they pass a validation check and match the setup recording retention policy.
 
 :::tip
 

docs/docs/configuration/restream.md

@@ -197,7 +197,7 @@ For cameras that support two-way talk, go2rtc will automatically establish an au
 To prevent this, you must configure two separate stream instances:
 
 1. One stream instance with `#backchannel=0` for Frigate's viewing, recording, and detection (prevents go2rtc from establishing the blocking backchannel)
-2. A second stream instance without `#backchannel=0` for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
+2. A second stream instance with no `#` parameters at all for two-way talk functionality (can be used by Frigate's WebRTC viewer or other applications)
 
 Configuration example:
 
@@ -215,6 +215,8 @@ In this configuration:
 - `front_door` stream is used by Frigate for viewing, recording, and detection. The `#backchannel=0` parameter prevents go2rtc from establishing the audio output backchannel, so it won't block two-way talk access.
 - `front_door_twoway` stream is used for two-way talk functionality. This stream can be used by Frigate's WebRTC viewer when two-way talk is enabled, or by other applications (like Home Assistant Advanced Camera Card) that need access to the camera's audio output channel.
 
+Any `#` parameter on a bare `rtsp://` source disables the backchannel unless the URL explicitly contains `#backchannel=1`. A two-way talk stream with something like `#video=h264` on it silently loses two-way audio, and Frigate will report that two-way talk is unavailable for that stream.
+
 ## Security: Restricted Stream Sources
 
 For security reasons, the `echo:`, `expr:`, and `exec:` stream sources are disabled by default in go2rtc. These sources allow arbitrary command execution and can pose security risks if misconfigured.

docs/docs/configuration/review.md

@@ -121,6 +121,31 @@ cameras:
 </TabItem>
 </ConfigTabs>
 
+## Categorizing manual events
+
+Events created with the [create manual event API](../integrations/api/create-event-events-camera-name-label-create-post.api.mdx) are categorized with the same label lists, using the label from the request path:
+
+1. If alerts are enabled and the label is listed in `review -> alerts -> labels`, the review item is an alert.
+2. Otherwise, if detections are enabled and the label is listed in `review -> detections -> labels`, the review item is a detection.
+3. If the label is in neither list, the review item is an alert, or no review item is created if alerts are disabled.
+
+This means manual events are alerts unless you explicitly list their label as a detection label. For example, to have PIR sensors create detections instead of alerts, post to `/api/events/front_door/pir_sensor/create` with the following config:
+
+```yaml {5-7}
+cameras:
+  front_door:
+    review:
+      detections:
+        labels:
+          - pir_sensor
+```
+
+:::note
+
+Required zones do not apply to manual events, since they are created through the API rather than by the object tracker. Setting `review -> alerts -> labels` to an empty list also does not stop manual events from becoming alerts, as a label in neither list still falls back to an alert.
+
+:::
+
 ## Restricting review items to specific zones
 
 By default a review item will be created if any `review -> alerts -> labels` and `review -> detections -> labels` are detected anywhere in the camera frame. You will likely want to configure review items to only be created when the object enters an area of interest, [see the zone docs for more information](./zones.md#restricting-alerts-and-detections-to-specific-zones)

docs/docs/configuration/semantic_search.md

@@ -163,8 +163,8 @@ genai:
     model: your-model-name
     roles:
       - embeddings
-      - vision
-      - tools
+      - descriptions
+      - chat
 
 semantic_search:
   enabled: True

docs/docs/frigate/installation.md

@@ -78,7 +78,7 @@ Users of the Snapcraft build of Docker cannot use storage locations outside your
 
 Frigate utilizes shared memory to store frames during processing. The default `shm-size` provided by Docker is **64MB**.
 
-The default shm size of **128MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in Docker Compose).
+The default shm size of **128MB** is fine for setups with **2 cameras** detecting at **720p**. If Frigate is exiting with "Bus error" messages, it is likely because you have too many high resolution cameras and you need to specify a higher shm size, using [`--shm-size`](https://docs.docker.com/engine/reference/run/#runtime-constraints-on-resources) (or [`service.shm_size`](https://docs.docker.com/compose/compose-file/compose-file-v2/#shm_size) in Docker Compose). If raising the shm size does not help, check your [process and file limits](#process-and-file-limits) as well.
 
 The Frigate container also stores logs in shm, which can take up to **40MB**, so make sure to take this into account in your math as well.
 
@@ -86,6 +86,30 @@ The Frigate container also stores logs in shm, which can take up to **40MB**, so
 
 The shm size cannot be set per container for Home Assistant Apps. However, this is probably not required since by default Home Assistant Supervisor allocates `/dev/shm` with half the size of your total memory. If your machine has 8GB of memory, chances are that Frigate will have access to up to 4GB without any additional configuration.
 
+### Process and file limits
+
+Frigate runs many processes and opens a number of shared memory files. Installs with a large number of cameras can exceed the default limits your container runtime applies.
+
+Hitting the PID limit logs `RuntimeError: can't start new thread`, often followed by a "Bus error" that makes it look like an shm sizing problem. Compare the current count against the max from inside the container:
+
+```bash
+cat /sys/fs/cgroup/pids.current
+cat /sys/fs/cgroup/pids.max
+```
+
+If these are close, raise the limit with [`--pids-limit`](https://docs.docker.com/engine/containers/resource_constraints/) (or `service.pids_limit` in Docker Compose).
+
+Running out of file descriptors logs `OSError: [Errno 24] Too many open files`. Raise the limit in Docker Compose:
+
+```yaml
+services:
+  frigate:
+    ulimits:
+      nofile:
+        soft: 65535
+        hard: 65535
+```
+
 ## Extra Steps for Specific Hardware
 
 The following sections contain additional setup steps that are only required if you are using specific hardware. If you are not using any of these hardware types, you can skip to the [Docker](#docker) installation section.
@@ -484,7 +508,6 @@ Generate a Frigate Docker Compose configuration based on your hardware and requi
 
 <DockerComposeGenerator/>
 
-
   </TabItem>
   <TabItem value="original" label="Example Docker Compose File">
 ```yaml

docs/docs/frigate/network_requirements.md

@@ -34,6 +34,12 @@ The following models are downloaded automatically the first time their associate
 | [Custom classification](/configuration/custom_classification/state_classification) (training) | MobileNetV2 ImageNet base weights (via Keras)                              | Google storage       |
 | [Audio transcription](/configuration/advanced/system)                                         | Whisper or Sherpa-ONNX streaming model                                     | HuggingFace / OpenAI |
 
+:::note
+
+The MobileNetV2 base weights are the one exception to the `/config/model_cache/` rule. They are also the only entry that is not downloaded when the feature is enabled: Frigate fetches them when a training run actually starts.
+
+:::
+
 ### Hardware-Specific Detector Models
 
 If you are using one of the following hardware detectors and have not provided your own model file, a default model will be downloaded on first startup:
@@ -75,7 +81,7 @@ If your Frigate instance has restricted internet access, you can point model dow
 | `HF_ENDPOINT`                       | `https://huggingface.co`            | Semantic search, Sherpa-ONNX, AXEngine models |
 | `GITHUB_ENDPOINT`                   | `https://github.com`                | Face recognition, LPR, RKNN models            |
 | `GITHUB_RAW_ENDPOINT`               | `https://raw.githubusercontent.com` | Bird classification                           |
-| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Google storage (Keras default)      | Custom classification training                |
+| `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` | Unset (Keras uses its own default)  | Custom classification training                |
 
 ## Optional Cloud Services
 
@@ -147,9 +153,23 @@ When running as a Home Assistant App, the go2rtc startup script queries the loca
 To run Frigate in an air-gapped or offline environment:
 
 1. **Pre-download models**: Start Frigate with internet access once with all desired features enabled. Models will be cached in `/config/model_cache/`.
-2. **Disable version check**: Set `telemetry.version_check: false` in your configuration.
-3. **Block outbound model requests**: Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
-4. **Avoid cloud features**: Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
-5. **Use local model mirrors**: If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, and `GITHUB_RAW_ENDPOINT` environment variables to point to local mirrors.
+2. **Pre-download the training base weights**: If you plan to train custom classification models, set `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` before training, then run one training job while online. Without this variable the base weights are cached outside `/config/` and are lost whenever the container is recreated, so a later training run will fail offline. If the machine never has internet access, copy the weights in manually as described below.
+3. **Disable version check**: Set `telemetry.version_check: false` in your configuration.
+4. **Block outbound model requests**: Set the `HF_HUB_OFFLINE=1` and `TRANSFORMERS_OFFLINE=1` environment variables to prevent HuggingFace and Transformers from attempting any network requests.
+5. **Avoid cloud features**: Do not configure Frigate+, Generative AI providers that require internet, or cloud MQTT brokers.
+6. **Use local model mirrors**: If limited internet is available, set the `HF_ENDPOINT`, `GITHUB_ENDPOINT`, `GITHUB_RAW_ENDPOINT`, and `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` environment variables to point to local mirrors.
 
 After these steps, Frigate will operate with no outbound internet connections.
+
+### Manually Copying the Training Base Weights
+
+On a machine with internet access, download the weights:
+
+```bash
+curl -L -o mobilenet_v2_weights.h5 \
+  "https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_0.35_224_no_top.h5"
+```
+
+Copy the file into your Frigate config volume as `/config/model_cache/MobileNet/mobilenet_v2_weights.h5`, keeping that exact filename, then set the environment variable `TF_KERAS_MOBILENET_V2_WEIGHTS_URL` in your Docker compose file to the URL above and restart Frigate.
+
+The variable must be set even though the URL is never contacted. If it is unset, Frigate ignores the copied file and asks Keras to download the weights instead.

docs/docs/guides/getting_started.md

@@ -144,7 +144,7 @@ At this point you should be able to start Frigate and a basic config will be cre
 
 ### Step 2: Add a camera
 
-Click the **Add Camera** button in <NavPath path="Settings > Global configuration > Camera management" /> to use the camera setup wizard to get your first camera added into Frigate.
+Click the **Add Camera** button in <NavPath path="Settings > Global configuration > Camera management" /> to use the camera setup wizard to get your first camera added into Frigate. See [Adding a camera with the Add Camera Wizard](../configuration/cameras.md#adding-a-camera-with-the-add-camera-wizard) for a walkthrough of each step.
 
 ### Step 3: Configure hardware acceleration (recommended)
 

docs/docs/integrations/home-assistant.md

@@ -9,6 +9,8 @@ The best way to integrate with Home Assistant is to use the [official integratio
 
 ### Preparation
 
+Frigate itself must be installed and running before setting up the integration. See the [installation documentation](../frigate/installation.md) for details.
+
 The Frigate integration requires the `mqtt` integration to be installed and
 manually configured first.
 
@@ -279,7 +281,7 @@ For advanced usecases, this behavior can be changed with the [RTSP URL
 template](#options) option. When set, this string will override the default stream
 address that is derived from the default behavior described above. This option supports
 [jinja2 templates](https://jinja.palletsprojects.com/) and has the `camera` dict
-variables from [Frigate API](../integrations/api)
+variables from [Frigate API](/integrations/api/frigate-http-api)
 available for the template. Note that no Home Assistant state is available to the
 template, only the camera dict from Frigate.
 

docs/docs/integrations/homekit.md

@@ -3,35 +3,100 @@ id: homekit
 title: HomeKit
 ---
 
-Frigate cameras can be integrated with Apple HomeKit through go2rtc. This allows you to view your camera streams directly in the Apple Home app on your iOS, iPadOS, macOS, and tvOS devices.
+Frigate cameras can be exported to Apple HomeKit through go2rtc. Each exported camera appears as an accessory in the Apple Home app on your iOS, iPadOS, macOS, and tvOS devices.
 
 ## Overview
 
-HomeKit integration is handled entirely through go2rtc, which is embedded in Frigate. go2rtc provides the necessary HomeKit Accessory Protocol (HAP) server to expose your cameras to HomeKit.
+Exporting cameras is handled entirely through go2rtc, which is embedded in Frigate. go2rtc provides the necessary HomeKit Accessory Protocol (HAP) server, so your camera is published to HomeKit as an accessory in its own right.
 
-## Setup
+:::note
 
-All HomeKit configuration and pairing should be done through the **go2rtc WebUI**.
+This is the opposite of importing a HomeKit camera. go2rtc can also pair with an existing HomeKit camera (Aqara, Eve, Eufy, and similar) and use it as a stream source, which is what the `add` page of the go2rtc WebUI is for. That page discovers HomeKit accessories on your network and will not list your Frigate cameras. It is not used for exporting.
 
-### Accessing the go2rtc WebUI
-
-The go2rtc WebUI is available at:
-
-```
-http://<frigate_host>:1984
-```
-
-Replace `<frigate_host>` with the IP address or hostname of your Frigate server.
-
-### Pairing Cameras
-
-1. Navigate to the go2rtc WebUI at `http://<frigate_host>:1984`
-2. Use the `add` section to add a new camera to HomeKit
-3. Follow the on-screen instructions to generate pairing codes for your cameras
+:::
 
 ## Requirements
 
-- Frigate must be accessible on your local network using host network_mode
-- Your iOS device must be on the same network as Frigate
-- Port 1984 must be accessible for the go2rtc WebUI
-- For detailed go2rtc configuration options, refer to the [go2rtc documentation](https://github.com/AlexxIT/go2rtc)
+- Frigate must be running with `network_mode: host` so that HomeKit can discover your cameras over mDNS
+- Your Apple device must be on the same network as Frigate
+- Port 1984 must be accessible so you can reach the go2rtc WebUI
+
+HomeKit also places strict limits on the stream itself. go2rtc passes your stream through without resizing or re-encoding it, so the stream you export must already meet these requirements:
+
+- **Video:** H.264 at 1920x1080, 1280x720, or 320x240
+- **Audio:** Opus, mono, 16 kHz
+
+A camera's full resolution stream usually does not qualify. See [Exporting a compatible stream](#exporting-a-compatible-stream) below.
+
+## Configuration
+
+HomeKit settings are stored in `/config/go2rtc_homekit.yml`. This is a separate file from your Frigate config, because go2rtc needs to write your pairings back to it when you pair a device.
+
+Edit it using the go2rtc config editor, which writes to that file directly:
+
+```
+http://<frigate_host>:1984/editor.html
+```
+
+Replace `<frigate_host>` with the IP address or hostname of your Frigate server. The editor will be empty until you add a HomeKit section, since this file holds only your HomeKit settings and not the rest of your go2rtc config.
+
+:::warning
+
+Do not put the `homekit:` section in the `go2rtc:` section of your Frigate config.
+
+Frigate regenerates that config on every startup, so go2rtc cannot save your pairings to it. Pairing will appear to succeed and then fail after the next restart with `PairVerify with unknown client_id`. If the section exists in both places, your saved pairings are erased on every restart.
+
+:::
+
+Add an entry for each camera you want to export. The key must match the name of a go2rtc stream, and the pin must be 8 digits. This is the number the Home app calls the setup code:
+
+```yaml
+homekit:
+  front_door:
+    name: Front Door
+    pin: "12345678"
+```
+
+If the key does not match a go2rtc stream, go2rtc logs `[homekit] missing stream:` at startup and the camera will not appear in the Home app.
+
+:::note
+
+go2rtc derives each accessory's HomeKit identity from this key, so renaming it later means the camera appears as a new accessory and has to be paired again. Settle on the name before you pair.
+
+:::
+
+Frigate keeps only the `homekit:` section of this file when it starts, so do not store streams or other go2rtc settings in it.
+
+### Exporting a compatible stream
+
+If a camera's stream does not meet the requirements listed above, define a scaled restream in your Frigate config and point HomeKit at that stream instead of the original:
+
+```yaml
+go2rtc:
+  streams:
+    front_door:
+      - rtsp://user:password@192.168.1.50:554/stream
+    front_door_homekit:
+      - "ffmpeg:front_door#video=h264#width=1280#height=720#audio=opus/16000"
+```
+
+```yaml
+# /config/go2rtc_homekit.yml
+homekit:
+  front_door_homekit:
+    name: Front Door
+    pin: "12345678"
+```
+
+Add `#hardware=cuda`, `#hardware=vaapi`, or the appropriate value for your system to transcode using your GPU. Note that NVENC cannot encode H.264 wider than 4096 pixels, so very wide streams must be scaled down as shown above rather than only re-encoded.
+
+## Pairing Cameras
+
+1. Restart Frigate after adding the `homekit:` section
+2. In the Apple Home app, choose **Add Accessory**, then **More options** to enter a code manually
+3. Select your camera and enter the pin you configured as the setup code
+4. Confirm that a `pairings:` list now appears under the camera in `/config/go2rtc_homekit.yml`
+
+Pairings are saved back to that file automatically. If step 4 shows no `pairings:` list, check the Frigate log for `[homekit] can't save`, which means the `homekit:` section is missing from `/config/go2rtc_homekit.yml`.
+
+For detailed go2rtc configuration options, refer to the [go2rtc documentation](https://github.com/AlexxIT/go2rtc).

docs/docs/integrations/mqtt.md

@@ -16,7 +16,7 @@ MQTT requires a network connection to your broker. This is typically local, but
 ### `frigate/available`
 
 Designed to be used as an availability topic with Home Assistant. Possible message are:
-"online": published when Frigate is running (on startup)
+"online": published once Frigate is running and has published its initial state. Note that this is published on every connection to the broker, so it is republished if the broker restarts or the connection drops and recovers, without Frigate itself restarting.
 "stopped": published when Frigate is stopped normally
 "offline": published automatically by the MQTT broker if Frigate disconnects unexpectedly (via MQTT Will Message)
 
@@ -292,7 +292,9 @@ Topic with the currently active profile name. Published value is the profile nam
 
 ### `frigate/notifications/set`
 
-Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
+Topic to turn notifications on and off for all cameras. Expected values are `ON` and `OFF`.
+
+Only available when notifications are enabled in the config. Not persisted across Frigate restarts.
 
 ### `frigate/notifications/state`
 
@@ -308,6 +310,8 @@ Publishes the current health status of each role that is enabled (`audio`, `dete
 - `offline`: Stream is offline and is being restarted
 - `disabled`: Camera is currently turned off (either at runtime via the `enabled/set` topic, or persistently via the configuration file). See [Camera state](/configuration/live#camera-state) for the distinction.
 
+These reflect the state of Frigate's process for that role, not the camera's reachability, so an unreachable camera alternates between `offline` and `online` as the watchdog restarts ffmpeg. Wait for the status to hold steady (for example with Home Assistant's `for:`) rather than acting on a single message.
+
 ### `frigate/<camera_name>/<object_name>`
 
 Publishes the count of objects for the camera for use as a sensor in Home Assistant.
@@ -390,6 +394,18 @@ Topic to turn audio detection for a camera on and off. Expected values are `ON`
 
 Topic with current state of audio detection for a camera. Published values are `ON` and `OFF`.
 
+### `frigate/<camera_name>/audio_transcription/set`
+
+Topic to turn [live audio transcription](/configuration/audio_detectors#live-transcription) for a camera on and off. Expected values are `ON` and `OFF`. Transcribed text is published to `frigate/<camera_name>/audio/transcription`.
+
+`ON` is ignored unless audio transcription is enabled in the config for the camera. Unlike the other camera toggles, this one is not persisted across Frigate restarts.
+
+**NOTE:** Requires audio detection and transcription to be enabled
+
+### `frigate/<camera_name>/audio_transcription/state`
+
+Topic with current state of live audio transcription for a camera. Published values are `ON` and `OFF`.
+
 ### `frigate/<camera_name>/recordings/set`
 
 Topic to turn recordings for a camera on and off. Expected values are `ON` and `OFF`. The change is persisted across Frigate restarts (see [Runtime toggle persistence](/configuration/live#runtime-toggle-persistence)).
@@ -556,16 +572,20 @@ Topic with current state of the Birdseye mode for a camera. Published values are
 
 ### `frigate/<camera_name>/notifications/set`
 
-Topic to turn notifications on and off. Expected values are `ON` and `OFF`.
+Topic to turn notifications for a camera on and off. Expected values are `ON` and `OFF`.
+
+`ON` is ignored unless notifications are enabled in the config for the camera. This is not persisted across Frigate restarts. It is the same control the UI labels **Suspend until restart**.
 
 ### `frigate/<camera_name>/notifications/state`
 
-Topic with current state of notifications. Published values are `ON` and `OFF`.
+Topic with current state of notifications. Published values are `ON` and `OFF`. This is the authoritative topic for whether a camera will notify.
 
 ### `frigate/<camera_name>/notifications/suspend`
 
-Topic to suspend notifications for a certain number of minutes. Expected value is an integer.
+Topic to suspend notifications for a certain number of minutes. Expected value is an integer. Separate from `notifications/set`: it does not change `notifications/state`, and is ignored while notifications are off.
 
 ### `frigate/<camera_name>/notifications/suspended`
 
-Topic with timestamp that notifications are suspended until. Published value is a UNIX timestamp, or 0 if notifications are not suspended.
+Topic with timestamp that notifications are suspended until. Published value is a UNIX timestamp, or 0 if there is no timed suspension.
+
+`0` does not mean notifications are enabled: `notifications/set` `OFF` clears the timed suspension, so this publishes `0` while `notifications/state` is `OFF`.

docs/docs/troubleshooting/common_errors.md

@@ -0,0 +1,240 @@
+---
+id: common_errors
+title: Common Error Messages
+---
+
+import FaqItem from "@site/src/components/FaqItem";
+
+This page is an index of error messages you might see in Frigate's logs, what each one means, and where to go next. It is organized by the kind of problem, not by which component logged the message.
+
+Two things to know before you start:
+
+- **Many of these messages come from FFmpeg, go2rtc, GPU drivers, or the operating system, not from Frigate itself.** Frigate captures and re-logs their output, so the log level shown in the Frigate UI does not always reflect the original severity.
+- **Wrapped errors put the real cause on the next line.** When Frigate logs a generic message like `Error occurred when attempting to maintain recording cache`, the actual exception is logged immediately after it. When a camera's FFmpeg process exits, Frigate logs `The following ffmpeg logs include the last 100 lines prior to exit` and dumps that camera's FFmpeg output. Always read those lines, they are where the answer usually is.
+
+## Camera connection and streams
+
+<FaqItem id="connection-refused-no-route-to-host-401-404" question="Connection refused / No route to host / 401 Unauthorized / 404 Not Found">
+
+These are FFmpeg errors about reaching the camera (or the go2rtc restream). `Connection refused` and `No route to host` mean nothing is listening at that address or the host is unreachable; `401 Unauthorized` is wrong credentials; `404 Not Found` is a wrong stream path (or a `restream` input pointing at a go2rtc stream name that does not exist). A camera that has hit its concurrent-connection limit can also return `refused` or `401` on a URL that works in VLC.
+
+See [go2rtc troubleshooting](/troubleshooting/go2rtc#1-read-the-go2rtc-logs) for how to isolate the stream.
+
+</FaqItem>
+
+<FaqItem id="no-frames-received-in-20-seconds" question="No frames received from <camera> in 20 seconds. Exiting ffmpeg...">
+
+FFmpeg is running but has stopped delivering video for 20 seconds, so Frigate's camera watchdog restarts it. The stream connected at least once, then went quiet: a camera reboot, a network drop, the camera evicting the connection, or a stalled decoder. If it repeats on a loop, the stream is unstable.
+
+</FaqItem>
+
+<FaqItem id="ffmpeg-process-crashed-unexpectedly" question="Ffmpeg process crashed unexpectedly for <camera>">
+
+The detect FFmpeg process exited on its own. This message is only the notification; the cause is in the 100 FFmpeg log lines Frigate dumps right after it (look for a `Failed to sync surface`, `Connection refused`, codec, or audio error in that block). Related watchdog messages include `<camera> exceeded fps limit`, which means the camera is delivering frames faster than `detect.fps` (usually a camera whose real frame rate differs from what is configured).
+
+</FaqItem>
+
+<FaqItem id="non-monotonically-increasing-dts" question="Non-monotonic DTS / non monotonically increasing dts to muxer / Queue input is backward in time">
+
+These are FFmpeg messages indicating the camera sent packets with out-of-order timestamps, either on the video or the audio stream. Timestamp jitter like this is common with WiFi cameras and restreamed or proxied sources; other causes are a camera "Smart Codec" / H.264+ / H.265+ mode or a camera clock that jumps. A sustained flood of these messages usually precedes the stream stalling and the watchdog restarting FFmpeg.
+
+In most cases, the fix is to improve the network, reduce system resource usage, or switch to non-WiFi cameras. In general, WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
+
+On the video stream, this can affect recordings: because they are copied without re-encoding, FFmpeg cannot fix the timestamps, and the segment muxer often splits early, producing one-second segments and a cache backlog. See [Recordings: segments are only 1 second long](/troubleshooting/recordings#segments-are-only-1-second-long).
+
+On the audio stream, the messages can come from the output's audio encoding. If the audio stream is the problem, it may help to have go2rtc transcode it by adding `#audio=aac` to the camera's go2rtc stream to produce clean timestamps for everything consuming the restream.
+
+</FaqItem>
+
+<FaqItem id="bad-cseq" question="RTP: PT=xx: bad cseq (packet loss / reordering)">
+
+An FFmpeg message meaning RTP packets arrived out of sequence, which almost always means the stream is using UDP transport. Frigate's RTSP presets force TCP, so seeing this points at a custom `input_args`, `preset-rtsp-udp`, or a go2rtc source that is not using TCP. Switch to TCP unless your camera is [UDP-only](/configuration/camera_specific#udp-only-cameras).
+
+</FaqItem>
+
+<FaqItem id="error-while-decoding-mb-non-existing-pps" question="error while decoding MB / non-existing PPS referenced (corrupt frames)">
+
+FFmpeg decoder messages meaning the received video bitstream was incomplete or damaged. A few of these at every stream start are normal (the decoder connected before the first keyframe) and Frigate discards them. A continuous stream of them means real packet loss, from Wi-Fi or a saturated link, an overloaded camera, or an FFmpeg restart loop caused by another problem. Fix the underlying instability rather than the message.
+
+</FaqItem>
+
+<FaqItem id="could-not-find-codec-parameters" question="Could not find codec parameters for stream ... unspecified size">
+
+An FFmpeg message meaning it probed the stream but never saw enough decodable video to determine the frame size, often because the probe window ended before the first keyframe on a long-GOP stream, or because the stream is not delivering usable video. If it is a Reolink HTTP stream, use `preset-http-reolink`, which raises the probe size for exactly this case.
+
+</FaqItem>
+
+## Recording
+
+<FaqItem id="no-new-recording-segments" question="No new recording segments were created (or: No new valid recording segments were created / No valid segments created since last invalid segment) for <camera> in the last 120s">
+
+Frigate's record watchdog is restarting the record FFmpeg process because the camera stopped producing usable recordings. The wording distinguishes the cases: `No new recording segments` means no new segment file reached the cache, so ffmpeg isn't getting video out of the record stream; the two `valid` variants mean recordings are arriving but keep failing validation. Either way the fault is on the camera or network side, and the restart is Frigate trying to recover.
+
+See [Recordings: no new recording segments were created](/troubleshooting/recordings#no-new-recording-segments-were-created).
+
+</FaqItem>
+
+<FaqItem id="invalid-or-missing-video-stream-in-segment" question="Invalid or missing video stream in segment. Discarding. / Discarding a corrupt recording segment / Failed to probe corrupt segment / Invalid recording segment detected">
+
+A cached recording segment failed validation and was deleted, either because it had no readable video stream or because its length was impossible. This nearly always means the camera stopped sending usable video partway through the segment: a camera that rebooted, dropped the connection, or ran out of simultaneous connections, or an unreliable link such as WiFi or a failing switch port. Broken camera timestamps (a "Smart Codec" / H.264+ mode) cause the corrupt-segment variants. The same stream failure trips the record watchdog, so the restarts above usually appear alongside these messages.
+
+See [Recordings: invalid or missing video stream in segment](/troubleshooting/recordings#invalid-or-missing-video-stream-in-segment).
+
+</FaqItem>
+
+<FaqItem id="incompatible-audio-codec" question="Recordings silently fail to save (incompatible audio codec)">
+
+Some camera audio codecs (G.711 variants such as `pcm_alaw` and `pcm_mulaw`) cannot be stored in an MP4 container, so segments never finalize even though live view works.
+
+See [Recordings: incompatible audio codec](/troubleshooting/recordings#incompatible-audio-codec-recordings-silently-fail-to-save) for the FFmpeg preset that transcodes the audio to AAC.
+
+</FaqItem>
+
+<FaqItem id="error-maintaining-recording-cache" question="Error occurred when attempting to maintain recording cache">
+
+A generic wrapper; the real exception is on the next log line. Frequently it is `[Errno 28] No space left on device` or `[Errno 17] File exists` on a network share.
+
+See [Recordings cache warnings and errors](/troubleshooting/recordings#i-see-the-message-error--error-occurred-when-attempting-to-maintain-recording-cache), which covers this message and the common `Errno` cases.
+
+</FaqItem>
+
+## Hardware acceleration
+
+<FaqItem id="failed-to-sync-surface" question="Failed to sync surface / Failed to download frame: -5 / Error while filtering">
+
+A VAAPI/QSV hardware frame-sync failure between FFmpeg and the GPU driver, not a Frigate bug. It usually appears when the detect stream is being scaled or decoded on the GPU.
+
+See [GPU: Failed to download frame: -5](/troubleshooting/gpu#failed-to-download-frame--5), which lists the fixes in order (switch VAAPI/QSV preset, change `LIBVA_DRIVER_NAME`, use an H.264 substream, match detect resolution and fps to the stream).
+
+</FaqItem>
+
+<FaqItem id="no-decoder-surfaces-left" question="No decoder surfaces left / Can't allocate a surface">
+
+Both mean the GPU ran out of decode surfaces: `No decoder surfaces left` is NVIDIA NVDEC, `Can't allocate a surface` is Intel QSV. This is surface-pool exhaustion, typically from too many concurrent hardware-decoded cameras on one GPU (consumer NVIDIA cards have a driver-enforced limit on simultaneous decode sessions). Reduce the number of cameras decoding on that GPU, decode some on the CPU, or move to hardware without the session cap.
+
+</FaqItem>
+
+<FaqItem id="nvidia-container-cli-nvml-error" question="nvidia-container-cli: nvml error: driver not loaded">
+
+This comes from the NVIDIA container runtime while starting the container, not from Frigate, and the container never starts. The NVIDIA driver is not loaded on the host. Confirm `nvidia-smi` works on the host itself (not inside the container) before troubleshooting Frigate. In a VM or LXC, the driver must be available inside the guest. See [Hardware: Nvidia GPU](/configuration/hardware_acceleration_video).
+
+</FaqItem>
+
+## Detectors and models
+
+<FaqItem id="illegal-instruction" question="Illegal instruction (core dumped)">
+
+The process was killed by the CPU for executing an unsupported instruction. There are two distinct causes in Frigate:
+
+- **A Coral EdgeTPU** on a newer kernel with an outdated gasket driver. See [EdgeTPU: Illegal instruction](/troubleshooting/edgetpu#attempting-to-load-tpu-as-pci--fatal-python-error-illegal-instruction).
+- **A CPU without AVX/AVX2**, when enabling semantic search, face recognition, license plate recognition, classification, or audio transcription. These features use libraries compiled with AVX and crash immediately on CPUs that lack it (commonly Intel Celeron/Pentium before the 2020 Tiger Lake generation). See the [CPU requirements](/frigate/planning_setup#cpu).
+
+</FaqItem>
+
+<FaqItem id="onnx-invalidprotobuf" question="ONNX Runtime InvalidProtobuf / failed to load model">
+
+ONNX Runtime could not parse the model file. The file exists but its contents are not a valid ONNX model, usually a corrupted or interrupted download in `model_cache`, or the wrong file pointed at by `model.path`. Delete the cached model file so Frigate re-downloads it, and confirm `model.path` points at an actual `.onnx` model. See [ONNX detector configuration](/configuration/object_detectors#onnx).
+
+</FaqItem>
+
+<FaqItem id="cuda-failure-999-901" question="CUDA failure 999 / CUDA failure 901">
+
+ONNX Runtime CUDA errors. `999` (`cudaErrorUnknown`) is a general, unrecoverable CUDA context failure, usually a driver/runtime version mismatch between the host and the container or a GPU in a bad state. `901` is a CUDA-graph capture error, which points at a custom model whose operations are not capture-safe. For `999`, align the host driver with the container's CUDA version and confirm the GPU is healthy.
+
+</FaqItem>
+
+<FaqItem id="openvino-no-supported-devices" question="Can't get OPTIMIZATION_CAPABILITIES property as no supported devices found">
+
+OpenVINO could not find the configured device (usually `GPU` or `NPU`). Most often the `/dev/dri` render node is not passed into the container, or the wrong render node is mapped when an iGPU and a discrete GPU coexist.
+
+See [GPU: no supported devices found](/troubleshooting/gpu#cant-get-optimization_capabilities-property-as-no-supported-devices-found).
+
+</FaqItem>
+
+## Memory and storage
+
+<FaqItem id="fatal-python-error-bus-error" question="Fatal Python error: Bus error">
+
+Frigate ran out of shared memory (`/dev/shm`). The container's `shm_size` is too small for the number and resolution of your detect streams, or you added cameras after startup without increasing it.
+
+See [Calculating required shm-size](/frigate/installation#calculating-required-shm-size). If you cannot increase `shm_size`, lowering the `SHM_MAX_FRAMES` environment variable reduces how many frames Frigate buffers per camera.
+
+</FaqItem>
+
+<FaqItem id="errno-28-no-space-left" question="[Errno 28] No space left on device">
+
+A filesystem is full: the recordings volume (`/media/frigate`), the cache tmpfs (`/tmp/cache`), or `/dev/shm`. Check which one, and note that inode exhaustion can produce this while `df -h` still shows free space.
+
+See [Recordings: No space left on device](/troubleshooting/recordings#i-see-the-message-error--error-occurred-when-attempting-to-maintain-recording-cache).
+
+</FaqItem>
+
+<FaqItem id="container-exits-with-no-logs" question="The container exits or restarts with no error in the logs">
+
+A silent exit is usually the host or container out-of-memory killer. Because `/dev/shm` and `/tmp/cache` are memory-backed, they count against the container's memory limit, so aggressive shm or cache sizing can trigger it. Give the container more memory, or reduce shm/cache sizing, and check the host's OOM messages (`dmesg`).
+
+</FaqItem>
+
+## Database
+
+<FaqItem id="database-is-locked" question="database is locked">
+
+SQLite could not acquire the write lock. Frigate's timeout already scales with camera count, so under normal local-disk operation this essentially only happens when the database is on a network share (SMB/NFS), where file locking is unreliable, or when two instances point at the same file.
+
+See [Database is locked](/troubleshooting/faqs#error-database-is-locked).
+
+</FaqItem>
+
+<FaqItem id="database-disk-image-is-malformed" question="database disk image is malformed">
+
+The SQLite database file is corrupted, typically after hard power loss, a network-share database, or a filesystem with unsafe write semantics. Frigate does not repair it automatically, but the database can usually be recovered by hand.
+
+**Stop Frigate first**, then work on the database file directly (by default `/config/frigate.db`). Start by checking what is actually wrong:
+
+```bash
+sqlite3 frigate.db "PRAGMA integrity_check;"
+```
+
+If the only problems reported are index-related (lines such as `row 14 missing from index recordings_path` or `non-unique entry in index ...`), rebuilding the indexes is usually enough and is the least destructive fix:
+
+```bash
+sqlite3 frigate.db "REINDEX;"
+```
+
+If the integrity check reports page or byte-level corruption instead (for example `Multiple uses for byte 2706 of page 142272`), dump the readable contents into a new database:
+
+```bash
+# dump what can still be read
+sqlite3 frigate.db .dump > frigate.dump
+
+# keep the corrupt file, then rebuild from the dump
+mv frigate.db frigate.db.bak
+cat frigate.dump | sqlite3 frigate.db
+
+# confirm the rebuilt database is clean, this should print "ok"
+sqlite3 frigate.db "PRAGMA integrity_check;"
+```
+
+Rows stored in the corrupted pages cannot be recovered, so expect to lose some tracked objects, review items, or thumbnails. Recordings themselves are files on disk and are not affected.
+
+As a last resort, stop Frigate, delete `frigate.db`, and restart. Frigate recreates it, but existing recordings lose all of their metadata. If a `backup.db` exists next to your database, Frigate wrote it before the last schema migration and restoring it recovers everything up to that point.
+
+Repeat corruption usually points at the underlying storage: move the database off a network share, and on Raspberry Pi check power delivery and the SD card or SSD.
+
+</FaqItem>
+
+## Startup and web access
+
+<FaqItem id="unable-to-start-frigate-in-safe-mode" question="Unable to start Frigate in safe mode / Starting Frigate in safe mode">
+
+When your config fails validation at startup, Frigate prints the validation errors (with line numbers), then starts in **safe mode**: a minimal configuration with no cameras and MQTT disabled, so the UI stays reachable. In safe mode the only available page is the Config Editor, which shows the validation errors so you can fix them, then save and restart. Note that recording retention and storage cleanup do **not** run while in safe mode, so do not leave a low-disk system sitting in it.
+
+`Unable to start Frigate in safe mode` means even the minimal config failed, which points at an error in your `auth`, `proxy`, or `database` section, or a config file that is not valid YAML at all. Safe mode is not sticky; fix the config and restart and Frigate returns to normal.
+
+</FaqItem>
+
+<FaqItem id="502-bad-gateway" question="502 Bad Gateway / connection refused to 127.0.0.1:5001">
+
+The web server is up but the Frigate backend (port 5001) is not answering yet. By far the most common reason is that the page was loaded during startup: the API binds last, after database migrations (which can take minutes on a large database), model downloads, and process startup, while the web server is already serving. Wait for startup to finish. If it persists, the backend has failed to start, and the reason is earlier in the logs. This also explains a `connection refused to 127.0.0.1:5001` seen while loading `/ws`, because every authenticated request first makes an auth subrequest to that port.
+
+</FaqItem>

docs/docs/troubleshooting/cpu.md

@@ -3,7 +3,31 @@ id: cpu
 title: High CPU Usage
 ---
 
-High CPU usage can impact Frigate's performance and responsiveness. This guide outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.
+High CPU usage can impact Frigate's performance and responsiveness. This guide explains how to interpret the CPU values Frigate reports and outlines the most effective configuration changes to help reduce CPU consumption and optimize resource usage.
+
+## Understanding Frigate's Reported CPU Usage
+
+Frigate's CPU percentages often look much higher than what the host reports. Usually both numbers are correct and are simply measured against different denominators, so confirm you actually have a problem before tuning anything.
+
+### Per-process values are relative to a single core
+
+The values Frigate reports for FFmpeg, capture, detect, detector, and other processes follow the same convention as `top`: 100% means one CPU core is fully saturated, not that the whole system is saturated. A multithreaded process such as FFmpeg can legitimately report well over 100%.
+
+Host and hypervisor tools instead report a percentage of the machine's total capacity across all cores. This includes `docker stats`, the `htop` summary, the Proxmox summary graph, the Unraid dashboard, Synology Resource Monitor, and Home Assistant's system monitor sensors. To reconcile the two:
+
+```
+host percentage ≈ (sum of Frigate's process percentages) / (number of cores)
+```
+
+On a 4 core system, an FFmpeg process reporting 100% is consuming one quarter of the machine, so the host will show roughly 25 to 30% once the remaining Frigate processes are included. That same 100% on a 16 core system is about 6%. Frigate's own warning thresholds use the per-core convention as well, so an FFmpeg process is flagged at 20% of a single core, not 20% of the system.
+
+### Instantaneous samples and averages measure different things
+
+Frigate collects stats every 15 seconds, and the `cpu` value covers only the interval since the previous collection. The `cpu_average` value in the stats API and MQTT payload is the average across the entire life of the process, and it is what the high CPU usage warnings are based on. Host dashboards generally plot data averaged over a longer window, so a single Frigate sample can show a peak that a host graph never displays. A process that has just started, such as FFmpeg after a camera reconnect, reports 0 until it has been sampled twice.
+
+### The system-wide value depends on what the container can see
+
+The system CPU value is read from `/proc/stat`. Under Docker that file belongs to the host, so the value covers the entire machine including workloads unrelated to Frigate, and it will not match `docker stats` for the Frigate container. Under an LXC container, lxcfs virtualizes `/proc/stat` and the value reflects only the cores assigned to the container. In a virtual machine, the guest sees only its assigned vCPUs while the hypervisor divides by every physical thread on the node, so guest and host percentages will not agree even when both are accurate.
 
 ## 1. Hardware Acceleration for Video Decoding
 
@@ -72,3 +96,19 @@ The model you use significantly impacts detector performance. Frigate provides d
 - Larger models (640x640): Slower inference, can sometimes have higher accuracy on very large objects that take up a majority of the frame.
 
 For more detail on picking the right size, see [Choosing a model size](../configuration/object_detectors.md#choosing-a-model-size).
+
+## 3. Reducing Detector CPU Usage
+
+**Priority: High**
+
+The **Detector CPU Usage** metric measures the CPU spent converting frames into the tensor format the model expects and post-processing the model's output. It does not include inference, so this value can be high even when you've configured a GPU, NPU, or Coral for object detection.
+
+This metric scales with how many detections per second Frigate runs and how expensive each one is to prepare. Tuning [motion detection](../configuration/motion_detection) is usually the first recommendation to reduce the number of detections. Additionally, you can:
+
+- **Lower `detect -> fps`.** 5 is the recommended value for nearly all cameras. Running at 10 doubles the frames eligible for detection and is one of the largest contributors to this metric.
+- **Use a 320x320 model.** A 640x640 model has 4 times as many pixels to transpose, convert, and copy on every inference.
+- **Prefer a model that takes integer input.** Models configured with `input_dtype: float` require each frame to be converted to float32 and normalized on the CPU first. Models taking `int` input, such as the tflite models used by the Edge TPU, skip that step.
+- **Do not match the detect resolution to the model resolution.** The detect stream should match your camera's aspect ratio, for example `1280x720`, not the model's input size. Frigate crops and scales regions of motion itself, so an oversized detect stream only adds work.
+- **Tune stationary object behavior.** Objects that never settle into a stationary state are re-detected continuously. Raising `detect -> stationary -> interval` reduces how often detection runs on objects that are already parked. See [stationary objects](../configuration/stationary_objects).
+
+Adding [more detector instances](#multiple-detector-instances) spreads this work across more CPU cores, but does not reduce the total CPU used.

docs/docs/troubleshooting/dummy-camera.md

@@ -39,7 +39,9 @@ The per-clip variation is typically quite low and is mostly an artifact of keyfr
 
 Debug Replay lets you re-run Frigate's detection pipeline against a section of recorded video without manually configuring a dummy camera. It automatically extracts the recording, creates a temporary camera with the same detection settings as the original, and loops the clip through the pipeline so you can observe detections in real time.
 
-Debug Replay isn't intended to be a one-stop pane for all Frigate diagnostics or a comprehensive debugging environment for every Frigate feature. It merely makes it easier to spin up a "dummy camera" and perform some common adjustments in real-time. You'll still need to use the normal tools (logs, an MQTT client, etc) to debug your feature.
+The replay camera behaves like a live camera feed rather than History's video player: it loops the clip continuously as Frigate analyzes it and has no playback controls, so you cannot pause, scrub, or step through it frame by frame. The Debug Replay camera does not save recordings or snapshots or surface anything in Explore, but it otherwise behaves like a regular camera, including running enrichments such as Face Recognition, LPR, and custom classification.
+
+Debug Replay isn't intended to be a one-stop pane for all Frigate diagnostics or a comprehensive debugging environment for every Frigate feature. It merely makes it easier to spin up a "dummy camera" and perform some common adjustments in real time. You'll still need to use the normal tools (logs, an MQTT client, etc) to debug your feature.
 
 ### When to use
 

docs/docs/troubleshooting/faqs.md

@@ -39,6 +39,20 @@ To do this efficiently the following setup is required:
 
 When this is done correctly, the GPU will do the decoding and scaling which will result in a small increase in CPU usage but with better results.
 
+### How can I rotate my camera's video feed?
+
+Rotation is best done in the camera's firmware settings (usually called rotate, flip, or corridor mode) so the video arrives already rotated and no extra processing is needed. Check there first.
+
+If your camera does not support rotation, go2rtc's ffmpeg module can rotate the stream with the `#rotate` parameter (`90`, `180`, `270`, or `-90`), but this is not recommended: rotation requires transcoding (re-encoding) the video, which significantly increases CPU usage, especially for high resolution streams.
+
+```yaml
+go2rtc:
+  streams:
+    my_camera: "ffmpeg:rtsp://user:password@192.168.1.10:554/stream#video=h264#hardware#rotate=90"
+```
+
+Point the camera's inputs at the restream as described in the [restream docs](/configuration/restream.md), and swap `detect -> width` and `detect -> height` to match the rotated resolution.
+
 ### My mjpeg stream or snapshots look green and crazy
 
 This almost always means that the width/height defined for your camera are not correct. Double check the resolution with VLC or another player. Also make sure you don't have the width and height values backwards.
@@ -65,9 +79,17 @@ This is because Frigate does not run in host mode so localhost points to the Fri
 
 ### How do I know if my camera is offline
 
-A camera being offline can be detected via MQTT or /api/stats, the camera_fps for any offline camera will be 0.
+Frigate publishes a per-role health status to [`frigate/<camera_name>/status/<role>`](/integrations/mqtt#frigatecamera_namestatusrole), where `<role>` is each enabled role on the camera (`detect`, `record`, and `audio`). The published value is one of:
 
-Also, Home Assistant will mark any offline camera as being unavailable when the camera is offline.
+- `online`: Frigate's process for that role is running normally
+- `offline`: the process is down and Frigate is restarting it
+- `disabled`: the camera is turned off, either at runtime or in the configuration file
+
+These reflect the state of Frigate's process for that role, not the camera's reachability, so an unreachable camera alternates between `offline` and `online` as the watchdog restarts ffmpeg. Wait for the status to hold steady (for example with Home Assistant's `for:`) rather than acting on a single message.
+
+Because the status is per role, a camera whose substream is fine but whose recording stream has dropped will report `online` for `detect` and `offline` for `record`. The status is republished whenever it changes.
+
+You can also detect an offline camera through `/api/stats`, where `camera_fps` will be 0.
 
 ### How can I view the Frigate log files without using the Web UI?
 
@@ -125,6 +147,12 @@ cameras:
       height: 720
 ```
 
+### What is the `version` key in my config file?
+
+`version` records the config format that your config was last migrated to. On startup Frigate compares it against the format the running version expects, and if it is older it copies your config to `/config/backup_config.yaml`, rewrites it to the new format, and updates `version` as the final step. A config with no `version` key is assumed to predate 0.14 and is migrated from there.
+
+Frigate manages this key for you, so do not set or edit it. Raising it makes Frigate skip migrations your config still needs, and lowering it re-runs migrations against config that has already been converted. Either can leave you with a config that no longer validates.
+
 ### Why does Frigate keep creating new tracked objects for my parked car?
 
 Stationary tracking is designed to _prevent_ this: a parked car should remain a single tracked object rather than generating new ones. If you're repeatedly getting new tracked objects for the same car, it's likely that Frigate is losing the object and re-detecting it as a new one.

docs/docs/troubleshooting/go2rtc.md

@@ -78,7 +78,9 @@ go2rtc:
 
 :::warning
 
-The `#`-modifiers (`#video=`, `#audio=`, `#hardware`, `#backchannel=0`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
+The transcoding modifiers (`#video=`, `#audio=`, `#hardware`, …) **only take effect on a source that is prefixed with `ffmpeg:`**. Adding them to a bare `rtsp://…#audio=opus` source does nothing: go2rtc ignores them. Likewise, when a source references another stream by name (e.g. `ffmpeg:back#audio=aac`), the name must match the stream key **exactly** (it is case sensitive), or the transcode is silently never produced. This is the single most common configuration mistake. In the Frigate UI, the **Use compatibility mode (ffmpeg)** toggle adds the `ffmpeg:` prefix for you.
+
+A bare `rtsp://` source reads a different set of modifiers: `#backchannel=`, `#media=`, `#timeout=`, and `#transport=`. These do nothing on an `ffmpeg:` source. Adding **any** modifier to a bare `rtsp://` source also disables the camera's backchannel unless the URL explicitly contains `#backchannel=1`, so a stream dedicated to two-way talk should carry no modifiers at all.
 
 :::
 
@@ -153,7 +155,7 @@ WebRTC is only attempted when MSE fails or when using a camera's two-way talk fe
 
 - **Codec mismatch**: WebRTC cannot carry H.265 or AAC. The stream backing the WebRTC view must provide Opus (or PCMA/PCMU) audio and H.264 video. Add an `ffmpeg:back#audio=opus` source as shown above.
 - **Port `8555` not reachable, or no candidates set**: WebRTC needs port `8555` (both TCP and UDP) open and a reachable candidate advertised. On Docker installs running on a custom/overlay network, go2rtc may advertise unreachable container IPs as ICE candidates; setting `webrtc.filters.candidates: []` and supplying only your host's LAN IP resolves this. See [WebRTC extra configuration](/configuration/live#webrtc-extra-configuration).
-- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
+- **Two-way talk** additionally requires a secure context (HTTPS or the authenticated port `8971`, because browsers block microphone access on plain HTTP). The camera's RTSP backchannel must also be handled correctly: go2rtc seizes the backchannel by default, which blocks two-way audio for other consumers and can inject static. Disable it on the primary stream with `#backchannel=0` and use a separate dedicated stream for talk, carrying no `#` modifiers of any kind, as documented in [preventing go2rtc from blocking two-way audio](/configuration/restream#two-way-talk-restream).
 
 ## High CPU usage
 

docs/docs/troubleshooting/recordings.md

@@ -3,6 +3,8 @@ id: recordings
 title: Recordings Errors
 ---
 
+import FaqItem from "@site/src/components/FaqItem";
+
 ## Why are my recordings not working? (empty Recordings, "No recordings found for this time")
 
 If Frigate shows live video but the History view is empty, or you see "No recordings found for this time", the cause is almost always in one of the three categories below. Segments are first written to the RAM cache and are only moved to disk if they match a retention policy _and_ the camera's `record` stream is producing valid, storable video. Work through the categories in order: retention configuration is by far the most common cause.
@@ -19,7 +21,7 @@ A healthy camera logs lines like `Copied /media/frigate/recordings/{segment_path
 
 ### Retention configuration issues
 
-#### Recording is enabled, but nothing is saved
+<FaqItem id="recording-is-enabled-but-nothing-is-saved" question="Recording is enabled, but nothing is saved">
 
 This is the single most common cause. Setting `record.enabled: True` on its own does **not** keep any footage: **continuous recording is disabled by default**, and segments in the cache are only moved to disk if they match a configured retention policy. You must configure at least one of `continuous`, `motion`, `alerts`, or `detections` retention.
 
@@ -34,7 +36,9 @@ record:
 
 See [Recording](/configuration/record) for the full set of common configurations, including reduced-storage and alerts-only setups.
 
-#### Motion or event-only recording keeps less than you expect
+</FaqItem>
+
+<FaqItem id="motion-or-event-only-recording-keeps-less-than-you-expect" question="Motion or event-only recording keeps less than you expect">
 
 If you only configured `motion`, `alerts`, or `detections` retention (with no `continuous`), Frigate keeps footage selectively based on the retention `mode`:
 
@@ -44,20 +48,26 @@ If you only configured `motion`, `alerts`, or `detections` retention (with no `c
 
 If you expected continuous footage but only configured motion/event retention, add a `continuous` retention period as shown above. To verify motion is actually being detected, watch the motion boxes in the debug view or the Motion Tuner in the UI.
 
-#### Alert and detection recordings require working object detection
+</FaqItem>
+
+<FaqItem id="alert-and-detection-recordings-require-working-object-detection" question="Alert and detection recordings require working object detection">
 
 `alerts` and `detections` retention only keep footage that overlaps a tracked object, so they depend on object detection running:
 
 - **Detection must be enabled.** If `detect: enabled: False`, no alerts or detections are ever created, so alert/detection retention keeps nothing. (Continuous and motion retention still work with detection disabled.)
 - **The object must be supported by your model.** If you track an object your model doesn't support (for example `deer` or `license_plate` on the default model), Frigate never detects it and never records for it. Check your logs for warnings such as `... is configured to track ['deer'] objects, which are not supported by the current model` and remove unsupported objects or switch to a model (e.g. [Frigate+](/plus/)) that includes them.
 
-#### You're following an outdated guide
+</FaqItem>
+
+<FaqItem id="youre-following-an-outdated-guide" question="You're following an outdated guide">
 
 Configuration keys change between major versions. The old `clips` config, for example, has not existed for a long time. If you copied a config from an old blog post or video, verify every key against the current [reference config](/configuration/advanced/reference).
 
+</FaqItem>
+
 ### Camera and stream issues
 
-#### Incompatible audio codec (recordings silently fail to save)
+<FaqItem id="incompatible-audio-codec-recordings-silently-fail-to-save" question="Incompatible audio codec (recordings silently fail to save)">
 
 Frigate stores recordings in an MP4 container, and some camera audio codecs (most commonly `pcm_alaw`, `pcm_mulaw`, or other G.711 variants) **cannot be placed in an MP4 container**. When this happens, ffmpeg fails to write the segment and no recording is saved, even though the live view works fine. This is a frequent cause on Tapo, TP-Link VIGI, and some Reolink cameras.
 
@@ -72,7 +82,9 @@ cameras:
         # or preset-record-generic to record with no audio
 ```
 
-#### The record stream isn't connecting
+</FaqItem>
+
+<FaqItem id="the-record-stream-isnt-connecting" question="The record stream isn't connecting">
 
 A message like `No new recording segments were created for <camera> in the last 120s` means ffmpeg cannot read the `record` stream. To diagnose:
 
@@ -81,17 +93,11 @@ A message like `No new recording segments were created for <camera> in the last
 - Test the exact RTSP URL (with the correct path, port, and credentials) in VLC or `ffplay`.
 - If you restream through go2rtc, make sure the `record` input path points at the correct go2rtc stream name. Copying a config between cameras without updating the stream name is a common mistake.
 
-#### Recordings play back with no video (or won't play at all)
-
-Frigate copies the `record` stream directly without re-encoding, so playback depends on your browser supporting the camera's codec. H265/HEVC recordings may not be playable in some browsers. If recordings appear as audio-only or a black screen, your camera is likely sending a codec your browser can't decode. Configure the camera to output **H264** for maximum compatibility.
-
-#### Segments are only ~1 second long
-
-If the record stream uses a "Smart Codec"/H.264+ mode or changes encoding parameters mid-stream, corrupt timestamps cause segments to be split far too frequently and fill the cache. This produces the "Too many unprocessed recording segments" warning. See [that section below](#i-see-the-message-warning--too-many-unprocessed-recording-segments-in-cache-for-camera-this-likely-indicates-an-issue-with-the-detect-stream) for the full diagnosis.
+</FaqItem>
 
 ### Storage and mounting issues
 
-#### The storage volume isn't mounted correctly
+<FaqItem id="the-storage-volume-isnt-mounted-correctly" question="The storage volume isn't mounted correctly">
 
 If the recordings volume (`/media/frigate`) points at the wrong location, isn't writable, or a network/encrypted mount failed to mount at boot, Frigate cannot save recordings, or it silently writes to the boot drive and then purges aggressively because the drive appears far smaller than expected.
 
@@ -100,21 +106,158 @@ If the recordings volume (`/media/frigate`) points at the wrong location, isn't
 - For a mount that may fail intermittently, protecting the mount point with `chattr +i` on an empty directory forces Frigate to error out (rather than silently writing to the boot drive) when the mount is missing.
 - Check `dmesg` and system logs for filesystem or I/O errors around the time recordings disappeared.
 
-If recordings _are_ being written but the copy is too slow to keep up, see the ["Unable to keep up with recording segments"](#i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest) section below.
+If recordings _are_ being written but the copy is too slow to keep up, see the ["Unable to keep up with recording segments"](#i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest) question below.
 
-## I have Frigate configured for motion recording only, but it still seems to be recording even with no motion. Why?
+</FaqItem>
 
-You'll want to:
+## Recordings won't play back
 
-- Make sure your camera's timestamp is masked out with a motion mask. Even if there is no motion occurring in your scene, your motion settings may be sensitive enough to count your timestamp as motion.
-- If you have audio detection enabled, keep in mind that audio that is heard above `min_volume` is considered motion.
-- [Tune your motion detection settings](/configuration/motion_detection) either by editing your config file or by using the UI's Motion Tuner.
+<FaqItem id="pipeline-error-decode" question={"Recordings won't play back: \"PIPELINE_ERROR_DECODE\" (or \"Media failed to decode\")"}>
 
-## I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...
+When a recording refuses to play in the Frigate UI and you see an error like `Failed to play recordings (error 3): PIPELINE_ERROR_DECODE`, the message is coming from **your browser**, not from Frigate. `PIPELINE_ERROR_DECODE` is emitted exclusively by the media pipeline in **Chromium-based browsers** (Chrome, Edge, Brave, Vivaldi, Opera, Arc, and the Android WebView used by many in-app browsers) when the browser cannot decode a video or audio packet in the recording. WebKit browsers (Safari) report the same underlying problem with a different message, usually `Media failed to decode` or `DECODER_ERROR_NOT_SUPPORTED`.
+
+Frigate copies the `record` stream to disk **without re-encoding it**, so the browser must decode exactly what your camera produced, and Chromium's decoder is far stricter about malformed or nonstandard media than VLC or ffmpeg.
+
+:::warning
+
+The same recording playing perfectly in VLC, decoding cleanly with `ffprobe`/`ffmpeg`, or having a valid MP4 container does **not** mean the browser can decode it. VLC and ffmpeg are much more tolerant of codec quirks and damaged packets than a browser's media pipeline, so a "valid" file can still trigger `PIPELINE_ERROR_DECODE`. This is outside of Frigate's control, because Frigate never modifies the recording stream.
+
+:::
+
+#### Step 1: Confirm it is a browser issue
+
+Open the same recording in **Firefox** or **Safari**. Firefox and Safari both use a different media engine and cannot produce `PIPELINE_ERROR_DECODE`, so if playback works there you have confirmed a client-side codec or decoder problem rather than a bad recording. Switching browsers is a workaround, not a fix; the remaining steps address the root cause so that Chromium browsers work too.
+
+#### Step 2: Rule out H.265 / HEVC
+
+Browser support for H.265 (HEVC) is limited and depends on the operating system, GPU, hardware acceleration, and browser version, which makes it the most common cause of this error. Options, in order of reliability:
+
+- **Record H.264 instead.** Configure the camera's `record`/main stream to output H.264, the most compatible codec across all browsers. See [camera settings recommendations](/configuration/live#camera-settings-recommendations).
+- **Transcode to H.264 with go2rtc.** If you must keep HEVC on the camera, have go2rtc re-encode the recording stream. This increases CPU usage; add `#hardware` to use the GPU where available:
+
+  ```yaml
+  go2rtc:
+    streams:
+      your_camera:
+        # transcode video to h264 and audio to aac; #hardware uses the GPU if available
+        - "ffmpeg:rtsp://user:password@CAMERA_IP:554/stream#video=h264#audio=aac#hardware"
+  cameras:
+    your_camera:
+      ffmpeg:
+        inputs:
+          - path: rtsp://127.0.0.1:8554/your_camera
+            input_args: preset-rtsp-restream
+            roles:
+              - record
+  ```
+
+  The `#video=h264` parameter only takes effect with the `ffmpeg:` source module; adding it to a plain `rtsp://` go2rtc source does nothing.
+
+- **Keep HEVC but improve compatibility.** If your browser and OS do support HEVC, set [`apple_compatibility`](/configuration/camera_specific#h265-cameras-via-safari) on the camera. Some players (Safari and other clients) require a specific HEVC stream format that this option corrects:
+
+  ```yaml
+  cameras:
+    your_camera:
+      ffmpeg:
+        apple_compatibility: true
+  ```
+
+  You may also need to enable HEVC and hardware decoding in the browser itself (for example, Chrome's Settings → System → "Use hardware acceleration when available"). HEVC hardware support varies widely by GPU, OS, and browser version.
+
+#### Step 3: Clean up damaged packets from the camera
+
+If the error is **intermittent** (the same recording plays after a page refresh, or fails only after playing for a while), the camera is most likely emitting occasional corrupt or malformed packets. Some camera models are more prone to this than others. Routing the stream through go2rtc's `ffmpeg` module often "cleans up" the stream enough for the browser to decode it, even without changing the codec:
+
+```yaml
+go2rtc:
+  streams:
+    your_camera:
+      - "ffmpeg:rtsp://user:password@CAMERA_IP:554/stream#video=h264#audio=aac"
+```
+
+#### Step 4: Fix incompatible or corrupt audio
+
+Audio is one of the most common culprits, and a decode failure on the audio track fails the whole recording. Make sure the camera outputs **AAC** audio, transcode the audio to AAC with go2rtc (`#audio=aac`), or drop audio entirely. See [Incompatible audio codec](#incompatible-audio-codec-recordings-silently-fail-to-save) for a preset-based way to do this.
+
+#### Step 5: Avoid "smart" / "+" codecs and check the keyframe interval
+
+- Disable any **"Smart Codec"**, **"H.264+"**, or **"H.265+"** feature in the camera. These nonstandard modes drop keyframes and change encoding parameters mid-stream, producing exactly the kind of packets a browser refuses to decode. (They also cause [short recording segments](#segments-are-only-1-second-long).)
+- Set the camera's **I-frame (keyframe) interval equal to the frame rate** (for example `20` for a 20 fps stream). Long keyframe intervals slow the start of playback and make decode errors more likely.
+
+#### Step 6: Consider bitrate and the client hardware
+
+The browser decodes the video locally, so a stream that is too demanding can fail on one device while playing on another:
+
+- A **very high bitrate or resolution** (for example a 4K/8MP HEVC main stream) can overwhelm a low-power tablet, phone, or SBC and stall the decoder. Test the same recording on a desktop; if it plays there, lower the camera's bitrate or record a lower-resolution profile.
+- Errors that name the client's GPU decoder, such as `VaapiVideoDecoder: failed Initialize()ing the frame pool`, indicate a browser hardware-decode problem. Toggling the browser's "Use hardware acceleration" setting (on or off) often resolves these.
+
+</FaqItem>
+
+<FaqItem id="recordings-play-back-with-no-video-or-wont-play-at-all" question="Recordings play back with no video (or won't play at all)">
+
+Frigate copies the `record` stream directly without re-encoding, so playback depends on your browser supporting the camera's codec. H265/HEVC recordings may not be playable in some browsers. If recordings appear as audio-only or a black screen, your camera is likely sending a codec your browser can't decode. Configure the camera to output **H264** for maximum compatibility.
+
+If playback instead fails with an explicit `PIPELINE_ERROR_DECODE` or `Media failed to decode` error, see [Recordings won't play back with "PIPELINE_ERROR_DECODE"](#pipeline-error-decode) above.
+
+</FaqItem>
+
+## Recording cache warnings and errors
+
+<FaqItem id="segments-are-only-1-second-long" question="Segments are only ~1 second long">
+
+If the record stream uses a "Smart Codec"/H.264+ mode or changes encoding parameters mid-stream, corrupt timestamps cause segments to be split far too frequently and fill the cache. This produces the "Too many unprocessed recording segments" warning. See [that question below](#i-see-the-message-warning--too-many-unprocessed-recording-segments-in-cache-for-camera-this-likely-indicates-an-issue-with-the-detect-stream) for the full diagnosis.
+
+</FaqItem>
+
+<FaqItem id="invalid-or-missing-video-stream-in-segment" question="I see the message: WARNING : Invalid or missing video stream in segment ... Discarding.">
+
+Every recording segment is validated before it leaves the cache. Frigate probes each finished `.mp4` in `/tmp/cache` and requires a readable video stream and a valid duration before moving to storage. A segment that fails is deleted, so those ~10 seconds of footage are lost. Three messages come from this check:
+
+- `Invalid or missing video stream in segment <path>. Discarding.` The segment holds no video, or could not be read at all.
+- `Failed to probe corrupt segment <path>` followed by `Discarding a corrupt recording segment: <path>`. The segment was read, but its length could not be determined.
+- `Discarding a corrupt recording segment: <path>` on its own. The segment's length is impossible (empty, or longer than ten minutes), which points at broken timestamps coming from the camera.
+
+For each one, the camera watchdog also logs `Invalid recording segment detected for <camera> at <timestamp>`.
+
+:::warning
+
+This is almost always a **camera or network problem**, not a Frigate one. A segment is only complete once ffmpeg has finished writing it, so anything that interrupts the stream partway through leaves behind a file that cannot be saved. Frigate is reporting the interruption, not causing it.
+
+:::
+
+#### Start with the camera and the network
+
+- **The camera dropped the connection.** Cameras reboot, reinitialize their stream when switching to night mode, and cut clients off when they are overloaded or out of simultaneous connections. Count everything pulling from the camera at once: Frigate's detect and record streams, go2rtc, a phone app, and any other NVR each use one. Routing all roles through a single [RTSP restream](/configuration/restream#reduce-connections-to-camera) so the camera only ever sees one connection often resolves this by itself.
+- **The link to the camera is unreliable.** WiFi cameras, powerline adapters, a saturated uplink, a failing switch port, or a marginal cable all produce this pattern, and usually only on one camera at a time. WiFi cameras are [not recommended](https://ipcamtalk.com/threads/multiple-cameras-high-bandwidth.77100/#post-861110).
+- **The camera cannot reliably send what it is being asked for.** A high bitrate 4K stream can be more than the camera's own hardware can encode and push out under load. Lower the bitrate, or record a lower-resolution profile.
+- **The camera is using a "Smart Codec", H.264+, or H.265+ mode.** These change encoding parameters mid-stream and produce the broken timestamps behind the corrupt-segment variant. Turn the mode off and set the camera's keyframe interval equal to its frame rate. See [Segments are only ~1 second long](#segments-are-only-1-second-long).
+
+Read the rest of the Frigate and/or go2rtc log around the **first** occurrence. When the camera or the network is at fault, other messages show up with it, such as `No frames received from <camera> in 20 seconds`, `Non-monotonic DTS`, `RTP: PT=xx: bad cseq`, `error while decoding MB`, or a connection timeout. Each of those is explained in [Common error messages](/troubleshooting/common_errors). To confirm the camera is the source, open its stream in the [go2rtc web interface](/troubleshooting/go2rtc) on port `1984` or play the same URL in VLC, and leave it running long enough for the failures to happen again.
+
+#### If the camera and network check out
+
+- **Audio the recording cannot store.** Some cameras send G.711 audio, which cannot be saved in an MP4 and stops segments from finalizing. See [Incompatible audio codec](#incompatible-audio-codec-recordings-silently-fail-to-save).
+- **Frigate itself was stopped or restarted.** A single warning per camera around a restart is expected and needs no action.
+- **The system ran out of room or memory.** A full `/tmp/cache`, or the host killing Frigate for using too much memory, cuts off the segment being written. Both leave other errors in the log alongside this one. See [No space left on device](#errno-28-no-space-left-on-device).
+
+</FaqItem>
+
+<FaqItem id="no-new-recording-segments-were-created" question="I see the message: ERROR : No new recording segments were created for <camera> in the last 120s. Restarting the ffmpeg record process...">
+
+When a camera stops producing usable recordings for two minutes, Frigate restarts that camera's record process to try to recover. The wording tells you how far the recordings got:
+
+- **`No new recording segments were created`**: no new segment file showed up in the cache at all, so ffmpeg isn't getting video out of the record stream. The camera is unreachable or refusing the connection, the stream URL, path, or credentials are wrong, or the camera accepted the connection and then sent nothing. See [The record stream isn't connecting](#the-record-stream-isnt-connecting).
+- **`No new valid recording segments were created`** and **`No valid segments created since last invalid segment`**: recordings are arriving, but they keep failing validation, so the camera is sending video that cannot be saved. See [Invalid or missing video stream in segment](#invalid-or-missing-video-stream-in-segment) above.
+
+The restart is Frigate recovering from a problem, not causing one. One of these after a camera reboot or a brief network drop is normal. Seeing them repeat every couple of minutes means the camera or the network is still failing, and the restarts can extend the damage, because each one cuts off the segment that was being written. Work from the earliest failure in that camera's log rather than from the restarts.
+
+</FaqItem>
+
+<FaqItem id="i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest" question="I see the message: WARNING : Unable to keep up with recording segments in cache for camera. Keeping the 5 most recent segments out of 6 and discarding the rest...">
 
 This warning means the recording maintainer cannot move recording segments from the RAM cache to disk fast enough. When the cache fills up, Frigate discards the oldest segments to avoid running out of memory and crashing, so you lose recorded footage. This is almost always a storage throughput or system resource problem. Work through the steps below to identify which.
 
-### Step 1: Enable recording debug logging
+#### Step 1: Enable recording debug logging
 
 The first step is to measure how long each segment takes to move from the RAM cache to disk. Enable debug logging for the recording maintainer:
 
@@ -132,14 +275,14 @@ DEBUG   : Copied /media/frigate/recordings/{segment_path} in 0.2 seconds.
 
 Let this run until the warnings begin to appear, so you can confirm whether the disk is actually slowing down at the moment the error occurs.
 
-### Step 2: Interpret the copy times
+#### Step 2: Interpret the copy times
 
 The copy duration tells you which direction to investigate:
 
 - **Consistently longer than ~1 second**: your storage cannot keep up with the incoming recordings. Continue with Steps 3–5 to diagnose the slow storage.
 - **Consistently well under 1 second**: storage is fast enough, and the problem is more likely CPU or resource contention. Skip to Step 6.
 
-### Step 3: Check RAM, swap, cache, and disk utilization
+#### Step 3: Check RAM, swap, cache, and disk utilization
 
 If CPU, RAM, disk throughput, or bus I/O is insufficient, nothing inside Frigate will help. Review each aspect of available system resources while the warnings are occurring.
 
@@ -175,19 +318,21 @@ services:
 
 NOTE: These are hard limits for the container, so be sure there is enough headroom above what `docker stats` shows for your container. It will immediately halt if it hits `<MAXRAM>`. In general, keeping all cache and tmp filespace in RAM is preferable to disk I/O where possible.
 
-### Step 4: Check your storage type
+#### Step 4: Check your storage type
 
 Mounting a network share is a popular option for storing recordings, but it can lead to reduced copy times and cause problems. Some users have found that using `NFS` instead of `SMB` considerably decreased copy times and fixed the issue. It is also important to ensure that the network connection between the device running Frigate and the network share is stable and fast. A saturated or unreliable link will stall copies.
 
-### Step 5: Check your mount options
+#### Step 5: Check your mount options
 
 Some users found that mounting a drive via `fstab` with the `sync` option dramatically reduced performance and led to this issue. Using `async` instead greatly reduced copy times.
 
-### Step 6: Rule out CPU load
+#### Step 6: Rule out CPU load
 
 If the copy times are consistently under 1 second but you still see the warning, the machine's CPU load is likely too high for Frigate to have the resources to keep up. Try temporarily shutting down other services, and any resource-intensive Frigate features, to see if the issue improves.
 
-## I see the message: WARNING : Too many unprocessed recording segments in cache for camera. This likely indicates an issue with the detect stream...
+</FaqItem>
+
+<FaqItem id="i-see-the-message-warning--too-many-unprocessed-recording-segments-in-cache-for-camera-this-likely-indicates-an-issue-with-the-detect-stream" question="I see the message: WARNING : Too many unprocessed recording segments in cache for camera. This likely indicates an issue with the detect stream...">
 
 This warning means that the detect stream for the affected camera has fallen behind or stopped processing frames. Frigate's recording cache holds segments waiting to be analyzed by the detector. When more than 6 segments pile up without being processed, Frigate discards the oldest ones to prevent the cache from filling up.
 
@@ -197,11 +342,11 @@ This error is a **symptom**, not the root cause. The actual cause is always logg
 
 :::
 
-### Step 1: Get the full logs
+#### Step 1: Get the full logs
 
 Collect complete Frigate logs from startup through the first occurrence of the error. Look for errors or warnings that appear **before** the "Too many unprocessed" messages begin. That is where the root cause will be found.
 
-### Step 2: Check the cache directory
+#### Step 2: Check the cache directory
 
 Exec into the Frigate container and inspect the recording cache:
 
@@ -211,7 +356,7 @@ docker exec -it frigate ls -la /tmp/cache
 
 Each camera should have a small number of `.mp4` segment files. If one camera has significantly more files than others, that camera is the source of the problem. A problem with a single camera can cascade and cause all cameras to show this error.
 
-### Step 3: Verify segment duration
+#### Step 3: Verify segment duration
 
 Recording segments should be approximately 10 seconds long. Run `ffprobe` on segments in the cache to check:
 
@@ -233,7 +378,7 @@ You don't have to run `ffprobe` by hand to catch this. Open a camera's **Camera
 
 :::
 
-### Step 4: Check for a stuck detector
+#### Step 4: Check for a stuck detector
 
 If the detect stream is not processing frames, segments will accumulate. Common causes:
 
@@ -242,7 +387,7 @@ If the detect stream is not processing frames, segments will accumulate. Common
 - **Model too large**: Use smaller model variants (e.g., YOLO `s` or `t` size, not `e` or `x`). Use 320x320 input size rather than 640x640 unless you have a powerful dedicated detector.
 - **Virtualization**: Running Frigate in a VM (especially Proxmox) can cause the detector to hang or stall. This is a known issue with GPU/TPU passthrough in virtualized environments and is not something Frigate can fix. Running Frigate in Docker on bare metal is recommended.
 
-### Step 5: Check for GPU hangs
+#### Step 5: Check for GPU hangs
 
 On the host machine, check `dmesg` for GPU-related errors:
 
@@ -252,7 +397,7 @@ dmesg | grep -i -E "gpu|drm|reset|hang"
 
 Messages like `trying reset from guc_exec_queue_timedout_job` or similar GPU reset/hang messages indicate a driver or hardware issue. Ensure your kernel and GPU drivers (especially Intel) are up to date.
 
-### Step 6: Verify hardware acceleration configuration
+#### Step 6: Verify hardware acceleration configuration
 
 An incorrect `hwaccel_args` preset can cause ffmpeg to fail silently or consume excessive CPU, starving the detector of resources.
 
@@ -260,11 +405,11 @@ An incorrect `hwaccel_args` preset can cause ffmpeg to fail silently or consume
 - For h265 cameras, use the corresponding h265 preset (e.g., `preset-intel-qsv-h265`).
 - Note that `hwaccel_args` are only relevant for the detect stream. Frigate does not decode the record stream.
 
-### Step 7: Verify go2rtc stream configuration
+#### Step 7: Verify go2rtc stream configuration
 
 Ensure that the ffmpeg source names in your go2rtc configuration match the correct camera stream. A misconfigured stream name (e.g., copying a config from one camera to another without updating the stream reference) will cause the wrong stream to be used or the stream to fail entirely.
 
-### Step 8: Check system resources
+#### Step 8: Check system resources
 
 If none of the above apply, the issue may be a general resource constraint. Monitor the following on your host:
 
@@ -275,7 +420,9 @@ If none of the above apply, the issue may be a general resource constraint. Moni
 
 Try temporarily disabling resource-intensive features like `genai` and `face_recognition` to see if the issue resolves. This can help isolate whether the detector is being starved of resources.
 
-## I see the message: ERROR : Error occurred when attempting to maintain recording cache
+</FaqItem>
+
+<FaqItem id="i-see-the-message-error--error-occurred-when-attempting-to-maintain-recording-cache" question="I see the message: ERROR : Error occurred when attempting to maintain recording cache">
 
 This message means the recording maintainer hit an error while moving segments from the cache to disk. It is a **generic wrapper**: the actual cause is always logged on the **very next line**. Frigate usually recovers and keeps running, but any affected segments are lost, so it is worth resolving.
 
@@ -287,27 +434,57 @@ Always read the line immediately following this message. `Error occurred when at
 
 Because these are operating-system-level errors, they must be resolved on the **host**, not within Frigate's configuration. The most common underlying errors are below.
 
-### [Errno 28] No space left on device
+#### [Errno 28] No space left on device
 
 The filesystem Frigate is writing to is full. Things to check:
 
 - **The recordings volume is genuinely full.** Check free space on the host with `df -h` for the path mapped to `/media/frigate`, and review the **Storage** page in the Frigate UI.
 - **The disk shows free space but is still "full".** This usually means the filesystem has run out of **inodes** (check with `df -i`), or recordings are landing on a different, smaller filesystem than you expect because of an incorrect bind mount. See [The storage volume isn't mounted correctly](#the-storage-volume-isnt-mounted-correctly) above.
-- **`/tmp/cache` is full.** If you mounted `/tmp/cache` as a small `tmpfs`, a backlog of segments can fill it. Increase the tmpfs size, or address whatever is causing segments to pile up (see the [Too many unprocessed recording segments](#i-see-the-message-warning--too-many-unprocessed-recording-segments-in-cache-for-camera-this-likely-indicates-an-issue-with-the-detect-stream) section above).
+- **`/tmp/cache` is full.** If you mounted `/tmp/cache` as a small `tmpfs`, a backlog of segments can fill it. Increase the tmpfs size, or address whatever is causing segments to pile up (see the [Too many unprocessed recording segments](#i-see-the-message-warning--too-many-unprocessed-recording-segments-in-cache-for-camera-this-likely-indicates-an-issue-with-the-detect-stream) question above).
 - **The host blocks writes before Frigate can purge.** On some systems (for example Unraid with a fill-up threshold), the host stops writes before Frigate's emergency cleanup can run. Leave more headroom on the volume, or lower your retention so Frigate purges sooner.
 
-### [Errno 17] File exists (with ffmpeg "Error writing trailer" or "unable to re-open output file")
+#### [Errno 17] File exists (with ffmpeg "Error writing trailer" or "unable to re-open output file")
 
 Errors like `[Errno 17] File exists: '/media/frigate/recordings/.../<camera>'`, often alongside ffmpeg errors such as `Unable to re-open ... output file for shifting data` or `Error writing trailer: No such file or directory`, are a hallmark of an **unreliable network share** (NFS or SMB). The mount is dropping, serving stale directory entries, or mishandling file locking.
 
 - Confirm the network connection to the NAS is stable and fast. An intermittent link produces these errors sporadically.
 - Prefer **NFS over SMB** for the recordings mount; several users have found NFS more reliable and faster.
-- Review your `fstab`/mount options for settings that hurt consistency or performance (see the `sync` vs `async` note in the [Unable to keep up with recording segments](#i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest) section above).
+- Review your `fstab`/mount options for settings that hurt consistency or performance (see the `sync` vs `async` note in the [Unable to keep up with recording segments](#i-see-the-message-warning--unable-to-keep-up-with-recording-segments-in-cache-for-camera-keeping-the-5-most-recent-segments-out-of-6-and-discarding-the-rest) question above).
 - Enable `frigate.record.maintainer` debug logging to confirm whether the errors line up with the share becoming unavailable.
 
-### Errors referencing a camera you manually renamed or removed
+#### Errors referencing a camera you manually renamed or removed
 
 If the next-line error references a camera name that no longer exists in your config, orphaned data is left over from a rename or removal in a persistent `/tmp/cache` volume.
 
 - Using a `tmpfs` mount for `/tmp/cache` as recommended in the [installation docs](/frigate/installation#storage) prevents stale cache files under the old camera name from surviving a restart, which avoids this issue entirely.
 - If errors persist, stop Frigate and remove any leftover segments for the old camera name from `/tmp/cache`.
+
+</FaqItem>
+
+## Other recording questions
+
+<FaqItem id="i-have-frigate-configured-for-motion-recording-only-but-it-still-seems-to-be-recording-even-with-no-motion-why" question="I have Frigate configured for motion recording only, but it still seems to be recording even with no motion. Why?">
+
+You'll want to:
+
+- Make sure your camera's timestamp is masked out with a motion mask. Even if there is no motion occurring in your scene, your motion settings may be sensitive enough to count your timestamp as motion.
+- If you have audio detection enabled, keep in mind that audio that is heard above `min_volume` is considered motion.
+- [Tune your motion detection settings](/configuration/motion_detection) either by editing your config file or by using the UI's Motion Tuner.
+
+</FaqItem>
+
+<FaqItem id="my-timeline-previews-are-black-after-restarting-frigate-or-recreating-the-container" question="My timeline previews are black after restarting Frigate or recreating the container. Why?">
+
+The scrubbing previews (the timelapse clips shown when dragging the History timeline, the secondary-camera previews, and the preview that plays when hovering a review card) are not recorded continuously. Frigate caches low-resolution preview frames in `/tmp/cache` throughout each hour and only assembles them into a finished preview clip **at the top of the hour**.
+
+In the recommended configuration, `/tmp/cache` is a small in-memory (`tmpfs`) area. When Frigate starts, it tries to restore the current hour's cached frames, so a **soft restart from the UI** preserves them. But if you recreate the Docker container or stop Frigate forcibly by any other means partway through an hour, the in-memory cache is discarded, so no preview clip is produced for that partial hour.
+
+This is expected behavior, not a bug:
+
+- Previews for hours that already completed and were written to disk are unaffected.
+- The next full hour after a restart will generate previews normally.
+- This is unrelated to `shm_size`; increasing shared memory does not change it.
+
+To avoid the gap, use the **Restart Frigate** button in the UI's Settings menu rather than recreating the container when possible.
+
+</FaqItem>

docs/docs/usage/exports.md

@@ -34,7 +34,7 @@ All of your exports live on the **Exports** page, reachable from the main naviga
 - **Rename** it, and
 - **Delete** it: deleting is the only way an export is removed.
 
-You can also select multiple exports at once to **delete** them in bulk, or to **add them to** (or **remove them from**) a [case](#cases).
+You can also select multiple exports at once to **delete** them in bulk, or to **add them to** (or **remove them from**) a [case](#cases). To download multiple exports as a zip archive, add them to a **case** and use the Download button there.
 
 ## Cases
 

docs/sidebars.ts

@@ -30,6 +30,7 @@ const sidebars: SidebarsConfig = {
     ],
     Configuration: [
       "configuration/config",
+      "configuration/config_overrides",
       {
         type: "category",
         label: "Detectors",
@@ -165,6 +166,7 @@ const sidebars: SidebarsConfig = {
     ],
     Troubleshooting: [
       "troubleshooting/faqs",
+      "troubleshooting/common_errors",
       "troubleshooting/go2rtc",
       "troubleshooting/recordings",
       "troubleshooting/dummy-camera",

docs/src/components/FaqItem/index.jsx

@@ -55,7 +55,7 @@ export default function FaqItem({ id, question, children }) {
           aria-controls={`${id}-content`}
           onClick={toggle}
         >
-          {question}
+          <span className={styles.question}>{question}</span>
         </button>
       </Heading>
       <div id={`${id}-content`} className={styles.content}>

docs/src/components/FaqItem/styles.module.css

@@ -44,6 +44,11 @@
   transform: rotate(45deg);
 }
 
+.question {
+  flex: 1;
+  min-width: 0;
+}
+
 .content {
   display: none;
   padding: 0 0 0.85rem;
@@ -61,7 +66,7 @@
 /* Desktop: render as a normal expanded heading + answer. */
 @media (min-width: 997px) {
   .heading {
-    margin: 1.75rem 0 0.5rem;
+    margin: 1.75rem 0 0.85rem;
   }
 
   .toggle {
@@ -78,7 +83,8 @@
 
   .content {
     display: block;
-    padding: 0;
+    padding: 0 0 0.5rem 1rem;
+    border-left: 2px solid var(--ifm-color-emphasis-200);
   }
 
   .heading :global(.hash-link) {

docs/static/frigate-api.yaml

@@ -693,6 +693,43 @@ paths:
         **Access:** Admin role required.
 
         Set a camera feature state. Use camera_name='*' to target all cameras.
+
+        The value to set is sent in the request body as `{"value": "<value>"}`.
+
+        | Feature | Accepted values |
+        | --- | --- |
+        | `enabled` | `ON`, `OFF` |
+        | `detect` | `ON`, `OFF` |
+        | `motion` | `ON`, `OFF` |
+        | `recordings` | `ON`, `OFF` |
+        | `snapshots` | `ON`, `OFF` |
+        | `audio` | `ON`, `OFF` |
+        | `audio_transcription` | `ON`, `OFF` |
+        | `notifications` | `ON`, `OFF` |
+        | `review_alerts` | `ON`, `OFF` |
+        | `review_detections` | `ON`, `OFF` |
+        | `object_descriptions` | `ON`, `OFF` |
+        | `review_descriptions` | `ON`, `OFF` |
+        | `improve_contrast` | `ON`, `OFF` |
+        | `ptz_autotracker` | `ON`, `OFF` |
+        | `birdseye` | `ON`, `OFF` |
+        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `motion_contour_area` | integer |
+        | `motion_threshold` | integer |
+        | `motion_mask` | `ON`, `OFF` |
+        | `object_mask` | `ON`, `OFF` |
+        | `zone` | `ON`, `OFF` |
+        | `profile` | a profile name, or `none` to deactivate |
+
+        `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+        parameter to be set to the name of the mask or zone. All other features
+        reject a sub-command.
+
+        `profile` applies globally rather than per camera, so it requires
+        `camera_name` to be `*`.
+
+        These features map to the equivalent MQTT topics, which document the
+        behavior of each value in more detail.
       operationId: 
         camera_set_camera__camera_name__set__feature___sub_command__put
       parameters:
@@ -746,6 +783,43 @@ paths:
         **Access:** Admin role required.
 
         Set a camera feature state. Use camera_name='*' to target all cameras.
+
+        The value to set is sent in the request body as `{"value": "<value>"}`.
+
+        | Feature | Accepted values |
+        | --- | --- |
+        | `enabled` | `ON`, `OFF` |
+        | `detect` | `ON`, `OFF` |
+        | `motion` | `ON`, `OFF` |
+        | `recordings` | `ON`, `OFF` |
+        | `snapshots` | `ON`, `OFF` |
+        | `audio` | `ON`, `OFF` |
+        | `audio_transcription` | `ON`, `OFF` |
+        | `notifications` | `ON`, `OFF` |
+        | `review_alerts` | `ON`, `OFF` |
+        | `review_detections` | `ON`, `OFF` |
+        | `object_descriptions` | `ON`, `OFF` |
+        | `review_descriptions` | `ON`, `OFF` |
+        | `improve_contrast` | `ON`, `OFF` |
+        | `ptz_autotracker` | `ON`, `OFF` |
+        | `birdseye` | `ON`, `OFF` |
+        | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+        | `motion_contour_area` | integer |
+        | `motion_threshold` | integer |
+        | `motion_mask` | `ON`, `OFF` |
+        | `object_mask` | `ON`, `OFF` |
+        | `zone` | `ON`, `OFF` |
+        | `profile` | a profile name, or `none` to deactivate |
+
+        `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+        parameter to be set to the name of the mask or zone. All other features
+        reject a sub-command.
+
+        `profile` applies globally rather than per camera, so it requires
+        `camera_name` to be `*`.
+
+        These features map to the equivalent MQTT topics, which document the
+        behavior of each value in more detail.
       operationId: camera_set_camera__camera_name__set__feature__put
       parameters:
         - name: camera_name
@@ -1402,7 +1476,7 @@ paths:
         - Classification
       summary: Get custom classification attributes
       description: |-
-        **Access:** Admin role required.
+        **Access:** Authenticated user with access to all cameras.
 
         Returns custom classification attributes for a given object type.
             Only includes models with classification_type set to 'attribute'.
@@ -1436,8 +1510,8 @@ paths:
               schema:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
-        - frigateAdminAuth: []
-      x-required-role: admin
+        - frigateUserAuth: []
+      x-required-role: all_cameras
   /classification/{name}/train:
     get:
       tags:
@@ -2234,15 +2308,15 @@ paths:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
         - frigateUserAuth: []
-      x-required-role: any
-      description: '**Access:** Any authenticated user.'
+      x-required-role: camera
+      description: '**Access:** Authenticated user with access to the referenced camera.'
   /review/summarize/start/{start_ts}/end/{end_ts}:
     post:
       tags:
         - Review
       summary: Generate Review Summary
       description: |-
-        **Access:** Admin role required.
+        **Access:** Authenticated user with access to all cameras.
 
         Use GenAI to summarize review items over a period of time.
       operationId: 
@@ -2273,8 +2347,8 @@ paths:
               schema:
                 $ref: '#/components/schemas/HTTPValidationError'
       security:
-        - frigateAdminAuth: []
-      x-required-role: admin
+        - frigateUserAuth: []
+      x-required-role: all_cameras
   /:
     get:
       tags:
@@ -5019,6 +5093,7 @@ paths:
             NOTES:
             - Creating a manual event does not trigger an update to /events MQTT topic.
             - If a duration is set to null, the event will need to be ended manually by calling /events/{event_id}/end.
+            - The review item is an alert unless the label is listed in the camera's review -> detections -> labels config.
       operationId: create_event_events__camera_name___label__create_post
       parameters:
         - name: camera_name
@@ -7034,7 +7109,9 @@ paths:
               schema:
                 $ref: '#/components/schemas/DebugReplayStartResponse'
         '400':
-          description: Invalid camera, time range, or no recordings
+          description: Invalid camera or time range
+        '404':
+          description: No recordings in the requested time range
         '409':
           description: A replay session is already active
         '422':
@@ -8244,6 +8321,11 @@ components:
       properties:
         provider:
           $ref: '#/components/schemas/GenAIProviderEnum'
+        name:
+          anyOf:
+            - type: string
+            - type: 'null'
+          title: Name
         api_key:
           anyOf:
             - type: string

frigate/api/app.py

@@ -31,6 +31,10 @@ from frigate.api.auth import (
     get_allowed_cameras_for_filter,
     require_role,
 )
+from frigate.api.config_util import (
+    publish_camera_section_updates,
+    swap_runtime_config,
+)
 from frigate.api.defs.query.app_query_parameters import AppTimelineHourlyQueryParameters
 from frigate.api.defs.request.app_body import (
     AppConfigSetBody,
@@ -195,7 +199,7 @@ def genai_models(request: Request):
         "before saving the configuration."
     ),
 )
-async def genai_probe(body: GenAIProbeBody):
+async def genai_probe(request: Request, body: GenAIProbeBody):
     load_providers()
 
     provider_cls = PROVIDERS.get(body.provider)
@@ -205,6 +209,13 @@ async def genai_probe(body: GenAIProbeBody):
             content={"success": False, "message": "Unknown provider"},
         )
 
+    api_key = body.api_key
+    if api_key == REDACTED_CREDENTIAL_SENTINEL:
+        saved_cfg = (
+            request.app.frigate_config.genai.get(body.name) if body.name else None
+        )
+        api_key = saved_cfg.api_key if saved_cfg else None
+
     # The OpenAI-compatible SDKs accept "timeout" as a constructor kwarg via
     # provider_options; other plugins use GenAIClient.timeout passed below.
     # Don't inject timeout for Gemini — its HttpOptions interprets the value
@@ -216,7 +227,7 @@ async def genai_probe(body: GenAIProbeBody):
     try:
         transient_cfg = GenAIConfig(
             provider=body.provider,
-            api_key=body.api_key,
+            api_key=api_key,
             base_url=body.base_url,
             provider_options=probe_provider_options,
             # model is required by the schema but irrelevant for listing.
@@ -915,19 +926,7 @@ def config_set(request: Request, body: AppConfigSetBody):
 
             if body.requires_restart == 0 or body.update_topic:
                 old_config: FrigateConfig = request.app.frigate_config
-                request.app.frigate_config = config
-                request.app.genai_manager.update_config(config)
-
-                if request.app.profile_manager is not None:
-                    request.app.profile_manager.update_config(config)
-
-                if request.app.stats_emitter is not None:
-                    request.app.stats_emitter.config = config
-
-                if request.app.dispatcher is not None:
-                    request.app.dispatcher.config = config
-                    for comm in request.app.dispatcher.comms:
-                        comm.config = config
+                swap_runtime_config(request.app, config)
 
                 if body.update_topic:
                     if body.update_topic.startswith("config/cameras/"):
@@ -967,11 +966,26 @@ def config_set(request: Request, body: AppConfigSetBody):
                             body.update_topic, settings
                         )
 
+                        # a config/cameras/* topic publishes camera copies, a
+                        # global topic the global object. FrigateConfig.parse
+                        # folds some global sections down into every camera,
+                        # and workers read both objects, so any such section
+                        # needs its camera copies sent alongside the global
+                        # publish above.
+                        if body.update_topic == "config/birdseye":
+                            publish_camera_section_updates(
+                                request.app, config, CameraConfigUpdateEnum.birdseye
+                            )
+
             return JSONResponse(
                 content=(
                     {
                         "success": True,
-                        "message": "Config successfully updated, restart to apply",
+                        "message": (
+                            "Config successfully updated"
+                            if body.requires_restart == 0
+                            else "Config successfully updated, restart to apply"
+                        ),
                     }
                 ),
                 status_code=200,

frigate/api/auth.py

@@ -31,7 +31,7 @@ from frigate.api.media_auth import (
     deny_response_for_media_uri,
     is_role_restricted,
 )
-from frigate.config import AuthConfig, NetworkingConfig, ProxyConfig
+from frigate.config import AuthConfig, ProxyConfig
 from frigate.const import CONFIG_DIR, JWT_SECRET_ENV_VAR, PASSWORD_HASH_ALGORITHM
 from frigate.models import User
 
@@ -85,6 +85,7 @@ def require_admin_by_default():
         "/sub_labels",
         "/plus/models",
         "/recognized_license_plates",
+        "/classification/attributes",
         "/timeline",
         "/timeline/hourly",
         "/recordings/storage",
@@ -415,7 +416,7 @@ def create_encoded_jwt(user, role, expiration, secret):
     )
 
 
-def set_jwt_cookie(response: Response, cookie_name, encoded_jwt, expiration, secure):
+def set_jwt_cookie(response: Response, cookie_name, encoded_jwt, max_age, secure):
     # TODO: ideally this would set secure as well, but that requires TLS
     # SameSite is intentionally left unset (browsers default to Lax). Setting
     # SameSite=Lax/Strict would stop the cookie from being sent in cross-origin
@@ -427,7 +428,7 @@ def set_jwt_cookie(response: Response, cookie_name, encoded_jwt, expiration, sec
         key=cookie_name,
         value=encoded_jwt,
         httponly=True,
-        expires=expiration,
+        max_age=max_age,
         secure=secure,
     )
 
@@ -620,18 +621,18 @@ def resolve_role(
 def auth(request: Request):
     auth_config: AuthConfig = request.app.frigate_config.auth
     proxy_config: ProxyConfig = request.app.frigate_config.proxy
-    networking_config: NetworkingConfig = request.app.frigate_config.networking
 
     success_response = Response("", status_code=202)
 
-    # handle case where internal port is a string with ip:port
-    internal_port = networking_config.listen.internal
-    if type(internal_port) is str:
-        internal_port = int(internal_port.split(":")[-1])
-
     # dont require auth if the request is on the internal port
-    # this header is set by Frigate's nginx proxy, so it cant be spoofed
-    if int(request.headers.get("x-server-port", default=0)) == internal_port:
+    # this header is set by Frigate's nginx proxy, so it cant be spoofed.
+    # the port is the boot-time snapshot rather than the live config value:
+    # nginx's listeners are fixed at container start, so an in-memory config
+    # change must never move the port that is trusted here
+    if (
+        int(request.headers.get("x-server-port", default=0))
+        == request.app.auth_internal_port
+    ):
         success_response.headers["remote-user"] = "anonymous"
         success_response.headers["remote-role"] = "admin"
         return success_response
@@ -762,7 +763,7 @@ def auth(request: Request):
                 success_response,
                 JWT_COOKIE_NAME,
                 new_encoded_jwt,
-                new_expiration,
+                JWT_SESSION_LENGTH,
                 JWT_COOKIE_SECURE,
             )
 
@@ -875,7 +876,11 @@ def login(request: Request, body: AppPostLoginBody):
         encoded_jwt = create_encoded_jwt(user, role, expiration, request.app.jwt_token)
         response = Response("", 200)
         set_jwt_cookie(
-            response, JWT_COOKIE_NAME, encoded_jwt, expiration, JWT_COOKIE_SECURE
+            response,
+            JWT_COOKIE_NAME,
+            encoded_jwt,
+            JWT_SESSION_LENGTH,
+            JWT_COOKIE_SECURE,
         )
         # Clear admin_first_time_login flag after successful admin login so the
         # UI stops showing the first-time login documentation link.
@@ -967,6 +972,7 @@ def delete_user(request: Request, username: str):
     summary="Update user password",
     description="Updates a user's password. Users can only change their own password unless they have admin role. Requires the current password to verify identity for non-admin users. Password must be at least 12 characters long. If user changes their own password, a new JWT cookie is automatically issued.",
 )
+@limiter.limit(limit_value=rateLimiter.get_limit)
 async def update_password(
     request: Request,
     username: str,
@@ -980,10 +986,11 @@ async def update_password(
     current_username = current_user.get("username")
     current_role = current_user.get("role")
 
-    # viewers can only change their own password
-    if current_role == "viewer" and current_username != username:
+    # Only admins may target another account. This has to cover every non-admin
+    # role rather than just viewer, since custom roles are arbitrary names
+    if current_role != "admin" and current_username != username:
         raise HTTPException(
-            status_code=403, detail="Viewers can only update their own password"
+            status_code=403, detail="Users can only update their own password"
         )
 
     HASH_ITERATIONS = request.app.frigate_config.auth.hash_iterations
@@ -1037,7 +1044,11 @@ async def update_password(
         )
         # Set new JWT cookie on response
         set_jwt_cookie(
-            response, JWT_COOKIE_NAME, encoded_jwt, expiration, JWT_COOKIE_SECURE
+            response,
+            JWT_COOKIE_NAME,
+            encoded_jwt,
+            JWT_SESSION_LENGTH,
+            JWT_COOKIE_SECURE,
         )
 
     return response
@@ -1243,3 +1254,23 @@ async def get_allowed_cameras_for_filter(request: Request):
     all_camera_names = set(request.app.frigate_config.cameras.keys())
     roles_dict = request.app.frigate_config.auth.roles
     return User.get_allowed_cameras(role, roles_dict, all_camera_names)
+
+
+async def require_full_camera_access(
+    request: Request,
+    allowed_cameras: list[str] = Depends(get_allowed_cameras_for_filter),
+):
+    """Dependency for endpoints returning data that spans every camera.
+
+    Some responses cannot be meaningfully scoped to a subset of cameras, so
+    rather than filter them the endpoint is limited to callers who can already
+    see every camera. Admin and viewer always qualify; a custom role qualifies
+    only when its camera list covers all configured cameras.
+    """
+    all_camera_names = set(request.app.frigate_config.cameras.keys())
+
+    if not all_camera_names.issubset(allowed_cameras):
+        raise HTTPException(
+            status_code=403,
+            detail="Access to all cameras is required for this endpoint",
+        )

frigate/api/camera.py

@@ -25,6 +25,7 @@ from frigate.api.auth import (
     require_go2rtc_stream_access,
     require_role,
 )
+from frigate.api.config_util import swap_runtime_config
 from frigate.api.defs.request.app_body import CameraSetBody
 from frigate.api.defs.tags import Tags
 from frigate.config import FrigateConfig
@@ -1254,9 +1255,14 @@ async def delete_camera(
                     status_code=500,
                 )
 
-            # Update runtime config
-            request.app.frigate_config = config
-            request.app.genai_manager.update_config(config)
+            # rebind every collaborator to the new config and re-layer runtime
+            # toggles for the surviving cameras, same as /api/config/set
+            swap_runtime_config(request.app, config)
+
+            # drop the deleted camera's persisted overrides so a camera later
+            # added under the same name doesn't inherit them
+            if request.app.dispatcher is not None:
+                request.app.dispatcher.clear_runtime_state_for_camera(camera_name)
 
             # Publish removal to stop ffmpeg processes and clean up runtime state
             request.app.config_publisher.publish_update(
@@ -1322,7 +1328,45 @@ def camera_set(
     body: CameraSetBody,
     sub_command: str | None = None,
 ):
-    """Set a camera feature state. Use camera_name='*' to target all cameras."""
+    """Set a camera feature state. Use camera_name='*' to target all cameras.
+
+    The value to set is sent in the request body as `{"value": "<value>"}`.
+
+    | Feature | Accepted values |
+    | --- | --- |
+    | `enabled` | `ON`, `OFF` |
+    | `detect` | `ON`, `OFF` |
+    | `motion` | `ON`, `OFF` |
+    | `recordings` | `ON`, `OFF` |
+    | `snapshots` | `ON`, `OFF` |
+    | `audio` | `ON`, `OFF` |
+    | `audio_transcription` | `ON`, `OFF` |
+    | `notifications` | `ON`, `OFF` |
+    | `review_alerts` | `ON`, `OFF` |
+    | `review_detections` | `ON`, `OFF` |
+    | `object_descriptions` | `ON`, `OFF` |
+    | `review_descriptions` | `ON`, `OFF` |
+    | `improve_contrast` | `ON`, `OFF` |
+    | `ptz_autotracker` | `ON`, `OFF` |
+    | `birdseye` | `ON`, `OFF` |
+    | `birdseye_mode` | `CONTINUOUS`, `MOTION`, `OBJECTS` |
+    | `motion_contour_area` | integer |
+    | `motion_threshold` | integer |
+    | `motion_mask` | `ON`, `OFF` |
+    | `object_mask` | `ON`, `OFF` |
+    | `zone` | `ON`, `OFF` |
+    | `profile` | a profile name, or `none` to deactivate |
+
+    `motion_mask`, `object_mask`, and `zone` require the `sub_command` path
+    parameter to be set to the name of the mask or zone. All other features
+    reject a sub-command.
+
+    `profile` applies globally rather than per camera, so it requires
+    `camera_name` to be `*`.
+
+    These features map to the equivalent MQTT topics, which document the
+    behavior of each value in more detail.
+    """
     dispatcher = request.app.dispatcher
     frigate_config: FrigateConfig = request.app.frigate_config
 

frigate/api/chat.py

@@ -7,7 +7,7 @@ import operator
 import time
 from datetime import datetime
 from functools import reduce
-from typing import Any
+from typing import Any, Literal
 
 import cv2
 from fastapi import APIRouter, Body, Depends, HTTPException, Request
@@ -37,6 +37,7 @@ from frigate.api.defs.response.chat_response import (
 from frigate.api.defs.tags import Tags
 from frigate.api.event import _build_attribute_filter_clause, events
 from frigate.config import FrigateConfig
+from frigate.config.classification import SemanticSearchModelEnum
 from frigate.genai.prompts import (
     build_chat_system_prompt,
     get_attribute_classifications,
@@ -86,10 +87,23 @@ def get_tools(request: Request) -> JSONResponse:
     tools = get_tool_definitions(
         semantic_search_enabled=semantic_search_enabled,
         attribute_classifications=attribute_classifications,
+        embeddings_language=_embeddings_language(config),
     )
     return JSONResponse(content={"tools": tools})
 
 
+def _embeddings_language(config: FrigateConfig) -> Literal["english", "multi"]:
+    """Return the language capability of the configured embeddings model.
+
+    JinaV1 is English-only; every other option (JinaV2 or a GenAI embeddings
+    provider) handles multiple languages.
+    """
+    if config.semantic_search.model == SemanticSearchModelEnum.jinav1:
+        return "english"
+
+    return "multi"
+
+
 def _resolve_zones(
     zones: list[str],
     config: FrigateConfig,
@@ -98,11 +112,14 @@ def _resolve_zones(
     """Map zone names to their canonical config keys, case-insensitively.
 
     LLMs frequently echo a user's casing ("Front Yard") instead of the
-    configured key ("front_yard"). The downstream zone filter is a SQLite GLOB
-    over the JSON-encoded zones column, which is case-sensitive — so an
-    unnormalized name silently returns zero matches. Build a lookup over the
-    relevant cameras' configured zones and substitute when we find a match;
-    unknown names pass through so behavior matches what the model asked for.
+    configured key ("front_yard"), or fall back to a zone's friendly name
+    ("Front Walkway") instead of its ID ("front_walk"). The downstream zone
+    filter is a SQLite GLOB over the JSON-encoded zones column, which stores
+    config keys and is case-sensitive — so an unnormalized name silently
+    returns zero matches. Build a lookup over the relevant cameras' configured
+    zones, keyed by both the config key and the friendly name, and substitute
+    when we find a match; unknown names pass through so behavior matches what
+    the model asked for.
     """
     if not zones:
         return zones
@@ -112,8 +129,11 @@ def _resolve_zones(
         camera_config = config.cameras.get(camera_id)
         if camera_config is None:
             continue
-        for zone_name in camera_config.zones.keys():
+        for zone_name, zone_config in camera_config.zones.items():
             lookup.setdefault(zone_name.lower(), zone_name)
+            lookup.setdefault(
+                zone_config.get_formatted_name(zone_name).lower(), zone_name
+            )
 
     return [lookup.get(z.lower(), z) for z in zones]
 
@@ -1134,6 +1154,7 @@ async def chat_completion(
     tools = get_tool_definitions(
         semantic_search_enabled=semantic_search_enabled,
         attribute_classifications=attribute_classifications,
+        embeddings_language=_embeddings_language(config),
     )
     conversation = []
 

frigate/api/classification.py

@@ -11,11 +11,10 @@ from typing import Any
 import cv2
 from fastapi import APIRouter, Depends, Request, UploadFile
 from fastapi.responses import JSONResponse
-from pathvalidate import sanitize_filename
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
-from frigate.api.auth import require_role
+from frigate.api.auth import require_full_camera_access, require_role
 from frigate.api.defs.request.classification_body import (
     AudioTranscriptionBody,
     DeleteFaceImagesBody,
@@ -43,12 +42,21 @@ from frigate.util.classification import (
     write_training_metadata,
 )
 from frigate.util.file import get_event_snapshot
+from frigate.util.path import safe_join, sanitize_path_component
 
 logger = logging.getLogger(__name__)
 
 router = APIRouter(tags=[Tags.classification])
 
 
+def invalid_name_response(value: str) -> JSONResponse:
+    """Response for a name that cannot be used as a path component."""
+    return JSONResponse(
+        content={"success": False, "message": f"Invalid name: {value}"},
+        status_code=400,
+    )
+
+
 @router.get(
     "/faces",
     response_model=FacesResponse,
@@ -98,9 +106,7 @@ def reclassify_face(request: Request, body: dict = None):
         )
 
     json: dict[str, Any] = body or {}
-    training_file = os.path.join(
-        FACE_DIR, f"train/{sanitize_filename(json.get('training_file', ''))}"
-    )
+    training_file = safe_join(FACE_DIR, "train", json.get("training_file", ""))
 
     if not training_file or not os.path.isfile(training_file):
         return JSONResponse(
@@ -150,8 +156,10 @@ def train_face(request: Request, name: str, body: dict = None):
         )
 
     json: dict[str, Any] = body or {}
-    training_file_name = sanitize_filename(json.get("training_file", ""))
-    training_file = os.path.join(FACE_DIR, f"train/{training_file_name}")
+    training_file_name = json.get("training_file", "")
+    training_file = (
+        safe_join(FACE_DIR, "train", training_file_name) if training_file_name else None
+    )
     event_id = json.get("event_id")
 
     if not training_file_name and not event_id:
@@ -165,7 +173,9 @@ def train_face(request: Request, name: str, body: dict = None):
             status_code=400,
         )
 
-    if training_file_name and not os.path.isfile(training_file):
+    if training_file_name and (
+        training_file is None or not os.path.isfile(training_file)
+    ):
         return JSONResponse(
             content=(
                 {
@@ -176,9 +186,13 @@ def train_face(request: Request, name: str, body: dict = None):
             status_code=404,
         )
 
-    sanitized_name = sanitize_filename(name)
+    sanitized_name = sanitize_path_component(name)
+    new_file_folder = safe_join(FACE_DIR, name)
+
+    if sanitized_name is None or new_file_folder is None:
+        return invalid_name_response(name)
+
     new_name = f"{sanitized_name}-{datetime.datetime.now().timestamp()}.webp"
-    new_file_folder = os.path.join(FACE_DIR, f"{sanitized_name}")
 
     os.makedirs(new_file_folder, exist_ok=True)
 
@@ -261,9 +275,12 @@ async def create_face(request: Request, name: str):
             content={"message": "Face recognition is not enabled.", "success": False},
         )
 
-    os.makedirs(
-        os.path.join(FACE_DIR, sanitize_filename(name.replace(" ", "_"))), exist_ok=True
-    )
+    face_folder = safe_join(FACE_DIR, name.replace(" ", "_"))
+
+    if face_folder is None:
+        return invalid_name_response(name)
+
+    os.makedirs(face_folder, exist_ok=True)
     return JSONResponse(
         status_code=200,
         content={"success": False, "message": "Successfully created face folder."},
@@ -287,6 +304,9 @@ def register_face(request: Request, name: str, file: UploadFile):
             content={"message": "Face recognition is not enabled.", "success": False},
         )
 
+    if sanitize_path_component(name) is None:
+        return invalid_name_response(name)
+
     context: EmbeddingsContext = request.app.embeddings
     result = None if context is None else context.register_face(name, file.file.read())
 
@@ -356,8 +376,8 @@ def reclassify_face_image(request: Request, name: str, body: dict = None):
         )
 
     json: dict[str, Any] = body or {}
-    image_id = sanitize_filename(json.get("id", ""))
-    new_name = sanitize_filename(json.get("new_name", ""))
+    image_id = sanitize_path_component(json.get("id", ""))
+    new_name = sanitize_path_component(json.get("new_name", ""))
 
     if not image_id or not new_name:
         return JSONResponse(
@@ -381,7 +401,12 @@ def reclassify_face_image(request: Request, name: str, body: dict = None):
             status_code=400,
         )
 
-    source_folder = os.path.join(FACE_DIR, sanitize_filename(name))
+    source_folder = safe_join(FACE_DIR, name)
+    target_folder = safe_join(FACE_DIR, new_name)
+
+    if source_folder is None or target_folder is None:
+        return invalid_name_response(name)
+
     source_file = os.path.join(source_folder, image_id)
 
     if not os.path.isfile(source_file):
@@ -396,7 +421,6 @@ def reclassify_face_image(request: Request, name: str, body: dict = None):
         )
 
     target_filename = f"{new_name}-{datetime.datetime.now().timestamp()}.webp"
-    target_folder = os.path.join(FACE_DIR, new_name)
 
     os.makedirs(target_folder, exist_ok=True)
     shutil.move(source_file, os.path.join(target_folder, target_filename))
@@ -430,8 +454,19 @@ def deregister_faces(request: Request, name: str, body: DeleteFaceImagesBody):
             content={"message": "Face recognition is not enabled.", "success": False},
         )
 
+    sanitized_name = sanitize_path_component(name)
+
+    if sanitized_name is None:
+        return invalid_name_response(name)
+
+    sanitized_ids = [
+        component
+        for component in map(sanitize_path_component, body.ids)
+        if component is not None
+    ]
+
     context: EmbeddingsContext = request.app.embeddings
-    context.delete_face_ids(name, map(lambda file: sanitize_filename(file), body.ids))
+    context.delete_face_ids(sanitized_name, sanitized_ids)
     return JSONResponse(
         content=({"success": True, "message": "Successfully deleted faces."}),
         status_code=200,
@@ -642,7 +677,11 @@ def transcribe_audio(request: Request, body: AudioTranscriptionBody):
 def get_classification_dataset(name: str):
     dataset_dict: dict[str, list[str]] = {}
 
-    dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(name), "dataset")
+    sanitized_name = sanitize_path_component(name)
+    dataset_dir = safe_join(CLIPS_DIR, name, "dataset")
+
+    if sanitized_name is None or dataset_dir is None:
+        return invalid_name_response(name)
 
     if not os.path.exists(dataset_dir):
         return JSONResponse(
@@ -664,8 +703,8 @@ def get_classification_dataset(name: str):
             dataset_dict[category_name].append(file)
 
     # Get training metadata
-    metadata = read_training_metadata(sanitize_filename(name))
-    current_image_count = get_dataset_image_count(sanitize_filename(name))
+    metadata = read_training_metadata(sanitized_name)
+    current_image_count = get_dataset_image_count(sanitized_name)
 
     if metadata is None:
         training_metadata = {
@@ -702,6 +741,7 @@ def get_classification_dataset(name: str):
 
 @router.get(
     "/classification/attributes",
+    dependencies=[Depends(require_full_camera_access)],
     summary="Get custom classification attributes",
     description="""Returns custom classification attributes for a given object type.
     Only includes models with classification_type set to 'attribute'.
@@ -729,8 +769,8 @@ def get_custom_attributes(
         if object_type is not None and object_type not in model_objects:
             continue
 
-        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
-        if not os.path.exists(dataset_dir):
+        dataset_dir = safe_join(CLIPS_DIR, model_key, "dataset")
+        if dataset_dir is None or not os.path.exists(dataset_dir):
             continue
 
         attributes = []
@@ -760,7 +800,10 @@ def get_custom_attributes(
     The name must exist in the classification models. Returns a success message or an error if the name is invalid.""",
 )
 def get_classification_images(name: str):
-    train_dir = os.path.join(CLIPS_DIR, sanitize_filename(name), "train")
+    train_dir = safe_join(CLIPS_DIR, name, "train")
+
+    if train_dir is None:
+        return invalid_name_response(name)
 
     if not os.path.exists(train_dir):
         return JSONResponse(status_code=200, content=[])
@@ -831,15 +874,17 @@ def delete_classification_dataset_images(
 
     json: dict[str, Any] = body or {}
     list_of_ids = json.get("ids", "")
-    folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(category)
-    )
+    sanitized_name = sanitize_path_component(name)
+    folder = safe_join(CLIPS_DIR, name, "dataset", category)
+
+    if sanitized_name is None or folder is None:
+        return invalid_name_response(name)
 
     deleted_count = 0
     for id in list_of_ids:
-        file_path = os.path.join(folder, sanitize_filename(id))
+        file_path = safe_join(folder, id)
 
-        if os.path.isfile(file_path):
+        if file_path and os.path.isfile(file_path):
             os.unlink(file_path)
             deleted_count += 1
 
@@ -850,7 +895,6 @@ def delete_classification_dataset_images(
     # This ensures the dataset is marked as changed after deletion
     # (even if the total count happens to be the same after adding and deleting)
     if deleted_count > 0:
-        sanitized_name = sanitize_filename(name)
         metadata = read_training_metadata(sanitized_name)
         if metadata:
             last_count = metadata.get("last_training_image_count", 0)
@@ -888,8 +932,8 @@ def reclassify_classification_image(
         )
 
     json: dict[str, Any] = body or {}
-    image_id = sanitize_filename(json.get("id", ""))
-    new_category = sanitize_filename(json.get("new_category", ""))
+    image_id = sanitize_path_component(json.get("id", ""))
+    new_category = sanitize_path_component(json.get("new_category", ""))
 
     if not image_id or not new_category:
         return JSONResponse(
@@ -913,10 +957,13 @@ def reclassify_classification_image(
             status_code=400,
         )
 
-    sanitized_name = sanitize_filename(name)
-    source_folder = os.path.join(
-        CLIPS_DIR, sanitized_name, "dataset", sanitize_filename(category)
-    )
+    sanitized_name = sanitize_path_component(name)
+    source_folder = safe_join(CLIPS_DIR, name, "dataset", category)
+    target_folder = safe_join(CLIPS_DIR, name, "dataset", new_category)
+
+    if sanitized_name is None or source_folder is None or target_folder is None:
+        return invalid_name_response(name)
+
     source_file = os.path.join(source_folder, image_id)
 
     if not os.path.isfile(source_file):
@@ -933,7 +980,6 @@ def reclassify_classification_image(
     random_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
     timestamp = datetime.datetime.now().timestamp()
     new_name = f"{new_category}-{timestamp}-{random_id}.png"
-    target_folder = os.path.join(CLIPS_DIR, sanitized_name, "dataset", new_category)
 
     os.makedirs(target_folder, exist_ok=True)
 
@@ -983,7 +1029,7 @@ def rename_classification_category(
         )
 
     json: dict[str, Any] = body or {}
-    new_category = sanitize_filename(json.get("new_category", ""))
+    new_category = sanitize_path_component(json.get("new_category", ""))
 
     if not new_category:
         return JSONResponse(
@@ -996,12 +1042,12 @@ def rename_classification_category(
             status_code=400,
         )
 
-    old_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(old_category)
-    )
-    new_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", new_category
-    )
+    sanitized_name = sanitize_path_component(name)
+    old_folder = safe_join(CLIPS_DIR, name, "dataset", old_category)
+    new_folder = safe_join(CLIPS_DIR, name, "dataset", new_category)
+
+    if sanitized_name is None or old_folder is None or new_folder is None:
+        return invalid_name_response(name)
 
     if not os.path.exists(old_folder):
         return JSONResponse(
@@ -1030,7 +1076,6 @@ def rename_classification_category(
 
         # Mark dataset as ready to train by resetting training metadata
         # This ensures the dataset is marked as changed after renaming
-        sanitized_name = sanitize_filename(name)
         write_training_metadata(sanitized_name, 0)
 
         return JSONResponse(
@@ -1078,13 +1123,20 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
         )
 
     json: dict[str, Any] = body or {}
-    category = sanitize_filename(json.get("category", ""))
-    training_file_name = sanitize_filename(json.get("training_file", ""))
-    training_file = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "train", training_file_name
+    category = sanitize_path_component(json.get("category", ""))
+    training_file_name = json.get("training_file", "")
+    training_file = (
+        safe_join(CLIPS_DIR, name, "train", training_file_name)
+        if training_file_name
+        else None
     )
 
-    if training_file_name and not os.path.isfile(training_file):
+    if category is None:
+        return invalid_name_response(json.get("category", ""))
+
+    if training_file_name and (
+        training_file is None or not os.path.isfile(training_file)
+    ):
         return JSONResponse(
             content=(
                 {
@@ -1098,9 +1150,10 @@ def categorize_classification_image(request: Request, name: str, body: dict = No
     random_id = "".join(random.choices(string.ascii_lowercase + string.digits, k=6))
     timestamp = datetime.datetime.now().timestamp()
     new_name = f"{category}-{timestamp}-{random_id}.png"
-    new_file_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", category
-    )
+    new_file_folder = safe_join(CLIPS_DIR, name, "dataset", category)
+
+    if new_file_folder is None:
+        return invalid_name_response(name)
 
     os.makedirs(new_file_folder, exist_ok=True)
 
@@ -1138,9 +1191,10 @@ def create_classification_category(request: Request, name: str, category: str):
             status_code=404,
         )
 
-    category_folder = os.path.join(
-        CLIPS_DIR, sanitize_filename(name), "dataset", sanitize_filename(category)
-    )
+    category_folder = safe_join(CLIPS_DIR, name, "dataset", category)
+
+    if category_folder is None:
+        return invalid_name_response(category)
 
     os.makedirs(category_folder, exist_ok=True)
 
@@ -1179,12 +1233,15 @@ def delete_classification_train_images(request: Request, name: str, body: dict =
 
     json: dict[str, Any] = body or {}
     list_of_ids = json.get("ids", "")
-    folder = os.path.join(CLIPS_DIR, sanitize_filename(name), "train")
+    folder = safe_join(CLIPS_DIR, name, "train")
+
+    if folder is None:
+        return invalid_name_response(name)
 
     for id in list_of_ids:
-        file_path = os.path.join(folder, sanitize_filename(id))
+        file_path = safe_join(folder, id)
 
-        if os.path.isfile(file_path):
+        if file_path and os.path.isfile(file_path):
             os.unlink(file_path)
 
     return JSONResponse(
@@ -1201,7 +1258,11 @@ def delete_classification_train_images(request: Request, name: str, body: dict =
 )
 async def generate_state_examples(request: Request, body: GenerateStateExamplesBody):
     """Generate examples for state classification."""
-    model_name = sanitize_filename(body.model_name)
+    model_name = sanitize_path_component(body.model_name)
+
+    if model_name is None:
+        return invalid_name_response(body.model_name)
+
     cameras_normalized = {
         camera_name: tuple(crop)
         for camera_name, crop in body.cameras.items()
@@ -1224,7 +1285,11 @@ async def generate_state_examples(request: Request, body: GenerateStateExamplesB
 )
 async def generate_object_examples(request: Request, body: GenerateObjectExamplesBody):
     """Generate examples for object classification."""
-    model_name = sanitize_filename(body.model_name)
+    model_name = sanitize_path_component(body.model_name)
+
+    if model_name is None:
+        return invalid_name_response(body.model_name)
+
     collect_object_classification_examples(model_name, body.label)
 
     return JSONResponse(
@@ -1243,10 +1308,16 @@ async def generate_object_examples(request: Request, body: GenerateObjectExample
     Returns a success message.""",
 )
 def delete_classification_model(request: Request, name: str):
-    sanitized_name = sanitize_filename(name)
+    # This endpoint intentionally accepts models that are not in the config, so
+    # there is no allow list to fall back on. Both paths below are recursive
+    # deletes, so an unusable name has to be rejected outright.
+    data_dir = safe_join(CLIPS_DIR, name)
+    model_dir = safe_join(MODEL_CACHE_DIR, name)
+
+    if data_dir is None or model_dir is None:
+        return invalid_name_response(name)
 
     # Delete the classification model's data directory in clips
-    data_dir = os.path.join(CLIPS_DIR, sanitized_name)
     if os.path.exists(data_dir):
         try:
             shutil.rmtree(data_dir)
@@ -1255,7 +1326,6 @@ def delete_classification_model(request: Request, name: str):
             logger.debug(f"Failed to delete data directory for {name}: {e}")
 
     # Delete the classification model's files in model_cache
-    model_dir = os.path.join(MODEL_CACHE_DIR, sanitized_name)
     if os.path.exists(model_dir):
         try:
             shutil.rmtree(model_dir)

frigate/api/config_util.py

@@ -0,0 +1,63 @@
+"""Shared helpers for applying a freshly parsed config to the running app."""
+
+from fastapi import FastAPI
+
+from frigate.config import FrigateConfig
+from frigate.config.camera.updater import (
+    CameraConfigUpdateEnum,
+    CameraConfigUpdateTopic,
+)
+
+
+def publish_camera_section_updates(
+    app: FastAPI, config: FrigateConfig, update_type: CameraConfigUpdateEnum
+) -> None:
+    """Broadcast every camera's re-resolved value for a global section.
+
+    Global sections are folded into each camera at parse time and the camera
+    copies are what workers read, so send them rather than leave a worker to
+    guess which cameras were inheriting.
+    """
+    for camera_name, camera_config in config.cameras.items():
+        settings = getattr(camera_config, update_type.name, None)
+
+        if settings is None:
+            continue
+
+        app.config_publisher.publish_update(
+            CameraConfigUpdateTopic(update_type, camera_name), settings
+        )
+
+
+def swap_runtime_config(app: FastAPI, config: FrigateConfig) -> None:
+    """Point every long-lived collaborator at a newly parsed config object.
+
+    Both /api/config/set and camera deletion re-parse yaml into a fresh
+    FrigateConfig and must rebind the same set of references, or the API and
+    the dispatcher drift onto different objects (the API reports one camera
+    state while the dispatcher acts on another). Runtime toggle overrides are
+    re-layered last: the swap rebuilt every camera from yaml, so without this a
+    camera the user turned off would silently come back on.
+    """
+    app.frigate_config = config
+
+    if app.config_holder is not None:
+        app.config_holder.set(config)
+
+    app.genai_manager.update_config(config)
+
+    if app.profile_manager is not None:
+        app.profile_manager.update_config(config)
+
+    if app.stats_emitter is not None:
+        app.stats_emitter.config = config
+
+    if app.dispatcher is not None:
+        app.dispatcher.config = config
+
+        for comm in app.dispatcher.comms:
+            comm.config = config
+
+        # workers still hold the live toggle values, so correct only the
+        # config object here rather than re-broadcasting every override
+        app.dispatcher.reapply_runtime_state_to_config()

frigate/api/debug_replay.py

@@ -13,6 +13,7 @@ from frigate.api.auth import require_role
 from frigate.api.defs.tags import Tags
 from frigate.jobs.debug_replay import (
     ExportDebugReplaySource,
+    NoRecordingsError,
     RecordingDebugReplaySource,
     start_debug_replay_job,
 )
@@ -74,7 +75,8 @@ class DebugReplayStopResponse(BaseModel):
     response_model=DebugReplayStartResponse,
     status_code=202,
     responses={
-        400: {"description": "Invalid camera, time range, or no recordings"},
+        400: {"description": "Invalid camera or time range"},
+        404: {"description": "No recordings in the requested time range"},
         409: {"description": "A replay session is already active"},
     },
     dependencies=[Depends(require_role(["admin"]))],
@@ -113,6 +115,14 @@ async def start_debug_replay(request: Request, body: DebugReplayStartBody):
             },
             status_code=409,
         )
+    except NoRecordingsError:
+        return JSONResponse(
+            content={
+                "success": False,
+                "message": "No recordings found in the selected time range",
+            },
+            status_code=404,
+        )
     except ValueError:
         logger.exception("Rejected debug replay start request")
         return JSONResponse(

frigate/api/defs/request/app_body.py

@@ -14,6 +14,7 @@ class AppConfigSetBody(BaseModel):
 
 class GenAIProbeBody(BaseModel):
     provider: GenAIProviderEnum
+    name: str | None = None
     api_key: str | None = None
     base_url: str | None = None
     provider_options: dict[str, Any] = Field(default_factory=dict)

frigate/api/event.py

@@ -16,7 +16,6 @@ import numpy as np
 from fastapi import APIRouter, Request
 from fastapi.params import Depends
 from fastapi.responses import JSONResponse
-from pathvalidate import sanitize_filename
 from peewee import JOIN, DoesNotExist, fn, operator
 from playhouse.shortcuts import model_to_dict
 
@@ -56,11 +55,12 @@ from frigate.api.defs.response.generic_response import GenericResponse
 from frigate.api.defs.tags import Tags
 from frigate.comms.event_metadata_updater import EventMetadataTypeEnum
 from frigate.config.classification import ObjectClassificationType
-from frigate.const import CLIPS_DIR, TRIGGER_DIR
+from frigate.const import CLIPS_DIR
 from frigate.embeddings import EmbeddingsContext
 from frigate.models import Event, ReviewSegment, Timeline, Trigger
 from frigate.track.object_processing import TrackedObject
 from frigate.util.file import get_event_thumbnail_bytes, load_event_snapshot_image
+from frigate.util.path import get_trigger_thumbnail_path, safe_join
 from frigate.util.time import get_dst_transitions, get_tz_modifiers
 
 logger = logging.getLogger(__name__)
@@ -1452,10 +1452,10 @@ async def set_attributes(
             continue
 
         # Get available labels from dataset directory
-        dataset_dir = os.path.join(CLIPS_DIR, sanitize_filename(model_key), "dataset")
+        dataset_dir = safe_join(CLIPS_DIR, model_key, "dataset")
         available_labels = set()
 
-        if os.path.exists(dataset_dir):
+        if dataset_dir and os.path.exists(dataset_dir):
             for category_name in os.listdir(dataset_dir):
                 category_dir = os.path.join(dataset_dir, category_name)
                 if os.path.isdir(category_dir):
@@ -1538,15 +1538,18 @@ async def set_description(
     event.data["description"] = new_description
     event.save()
 
-    # If semantic search is enabled, update the index
-    if request.app.frigate_config.semantic_search.enabled:
-        context: EmbeddingsContext = request.app.embeddings
+    context: EmbeddingsContext | None = request.app.embeddings
+
+    if context is not None:
         if len(new_description) > 0:
-            context.update_description(
-                event_id,
-                new_description,
-            )
+            # If semantic search is enabled, update the index
+            if request.app.frigate_config.semantic_search.enabled:
+                context.update_description(
+                    event_id,
+                    new_description,
+                )
         else:
+            # embeddings are always cleaned up so they don't outlive their description
             context.db.delete_embeddings_description(event_ids=[event_id])
 
     response_message = (
@@ -1675,9 +1678,11 @@ async def delete_single_event(event_id: str, request: Request) -> dict:
     event.delete_instance()
     Timeline.delete().where(Timeline.source_id == event_id).execute()
 
-    # If semantic search is enabled, update the index
-    if request.app.frigate_config.semantic_search.enabled:
-        context: EmbeddingsContext = request.app.embeddings
+    # embeddings are always cleaned up, even when semantic search is disabled,
+    # so that they don't outlive their events
+    context: EmbeddingsContext | None = request.app.embeddings
+
+    if context is not None:
         context.db.delete_embeddings_thumbnail(event_ids=[event_id])
         context.db.delete_embeddings_description(event_ids=[event_id])
 
@@ -1743,6 +1748,7 @@ async def delete_events(request: Request, body: EventsDeleteBody):
     NOTES:
     - Creating a manual event does not trigger an update to /events MQTT topic.
     - If a duration is set to null, the event will need to be ended manually by calling /events/{event_id}/end.
+    - The review item is an alert unless the label is listed in the camera's review -> detections -> labels config.
     """,
 )
 def create_event(
@@ -1953,18 +1959,13 @@ def create_trigger_embedding(
         if body.type == "thumbnail":
             # Save image to the triggers directory
             try:
-                os.makedirs(
-                    os.path.join(TRIGGER_DIR, sanitize_filename(camera_name)),
-                    exist_ok=True,
-                )
-                with open(
-                    os.path.join(
-                        TRIGGER_DIR,
-                        sanitize_filename(camera_name),
-                        f"{sanitize_filename(body.data)}.webp",
-                    ),
-                    "wb",
-                ) as f:
+                webp_path = get_trigger_thumbnail_path(camera_name, body.data)
+
+                if webp_path is None:
+                    raise ValueError(f"Invalid trigger thumbnail path for {body.data}")
+
+                os.makedirs(os.path.dirname(webp_path), exist_ok=True)
+                with open(webp_path, "wb") as f:
                     f.write(thumbnail)
                 logger.debug(
                     f"Writing thumbnail for trigger with data {body.data} in {camera_name}."
@@ -2036,10 +2037,16 @@ def update_trigger_embedding(
         if body.type == "description":
             embedding = context.generate_description_embedding(body.data)
         elif body.type == "thumbnail":
-            webp_file = sanitize_filename(body.data) + ".webp"
-            webp_path = os.path.join(
-                TRIGGER_DIR, sanitize_filename(camera_name), webp_file
-            )
+            webp_path = get_trigger_thumbnail_path(camera_name, body.data)
+
+            if webp_path is None:
+                return JSONResponse(
+                    content={
+                        "success": False,
+                        "message": f"Invalid data for {body.type} trigger",
+                    },
+                    status_code=400,
+                )
 
             try:
                 event: Event = Event.get(Event.id == body.data)
@@ -2096,13 +2103,14 @@ def update_trigger_embedding(
             # Update existing trigger
             if trigger.data != body.data:  # Delete old thumbnail only if data changes
                 try:
-                    os.remove(
-                        os.path.join(
-                            TRIGGER_DIR,
-                            sanitize_filename(camera_name),
-                            f"{trigger.data}.webp",
+                    old_path = get_trigger_thumbnail_path(camera_name, trigger.data)
+
+                    if old_path is None:
+                        raise ValueError(
+                            f"Invalid trigger thumbnail path for {trigger.data}"
                         )
-                    )
+
+                    os.remove(old_path)
                     logger.debug(
                         f"Deleted thumbnail for trigger with data {trigger.data} in {camera_name}."
                     )
@@ -2136,12 +2144,13 @@ def update_trigger_embedding(
         if body.type == "thumbnail":
             # Save image to the triggers directory
             try:
-                camera_path = os.path.join(TRIGGER_DIR, sanitize_filename(camera_name))
-                os.makedirs(camera_path, exist_ok=True)
-                with open(
-                    os.path.join(camera_path, f"{sanitize_filename(body.data)}.webp"),
-                    "wb",
-                ) as f:
+                thumbnail_path = get_trigger_thumbnail_path(camera_name, body.data)
+
+                if thumbnail_path is None:
+                    raise ValueError(f"Invalid trigger thumbnail path for {body.data}")
+
+                os.makedirs(os.path.dirname(thumbnail_path), exist_ok=True)
+                with open(thumbnail_path, "wb") as f:
                     f.write(thumbnail)
                 logger.debug(
                     f"Writing thumbnail for trigger with data {body.data} in {camera_name}."
@@ -2212,11 +2221,12 @@ def delete_trigger_embedding(
             )
 
         try:
-            os.remove(
-                os.path.join(
-                    TRIGGER_DIR, sanitize_filename(camera_name), f"{trigger.data}.webp"
-                )
-            )
+            thumbnail_path = get_trigger_thumbnail_path(camera_name, trigger.data)
+
+            if thumbnail_path is None:
+                raise ValueError(f"Invalid trigger thumbnail path for {trigger.data}")
+
+            os.remove(thumbnail_path)
             logger.debug(
                 f"Deleted thumbnail for trigger with data {trigger.data} in {camera_name}."
             )

frigate/api/export.py

@@ -9,11 +9,12 @@ import zipfile
 from collections import deque
 from collections.abc import Iterator
 from pathlib import Path
+from urllib.parse import quote
 
 import psutil
 from fastapi import APIRouter, Depends, Query, Request
 from fastapi.responses import JSONResponse, StreamingResponse
-from pathvalidate import sanitize_filename, sanitize_filepath
+from pathvalidate import sanitize_filename
 from peewee import DoesNotExist
 from playhouse.shortcuts import model_to_dict
 
@@ -72,6 +73,7 @@ from frigate.record.export import (
     PlaybackSourceEnum,
     validate_ffmpeg_args,
 )
+from frigate.util.path import sanitize_contained_path
 from frigate.util.time import is_current_hour
 
 logger = logging.getLogger(__name__)
@@ -129,18 +131,12 @@ def _validate_export_case(export_case_id: str | None) -> JSONResponse | None:
 def _sanitize_existing_image(
     image_path: str | None,
 ) -> tuple[str | None, JSONResponse | None]:
-    # sanitize_filepath normalizes "\" to "/" but leaves ".." intact, so a path
-    # like "clips\..\..\etc/passwd" passes the CLIPS_DIR prefix check yet still
-    # escapes the directory once resolved. A valid snapshot path never uses "..".
-    if image_path and ".." in image_path:
-        return None, JSONResponse(
-            content={"success": False, "message": "Invalid image path"},
-            status_code=400,
-        )
+    if not image_path:
+        return None, None
 
-    existing_image = sanitize_filepath(image_path) if image_path else None
+    existing_image = sanitize_contained_path(image_path, CLIPS_DIR)
 
-    if existing_image and not existing_image.startswith(CLIPS_DIR):
+    if existing_image is None:
         return None, JSONResponse(
             content={"success": False, "message": "Invalid image path"},
             status_code=400,
@@ -458,6 +454,22 @@ def _stream_case_archive(exports: list[Export]) -> Iterator[bytes]:
     yield from buffer.drain()
 
 
+def _content_disposition(filename: str, ascii_fallback: str) -> str:
+    """Build an attachment Content-Disposition that survives non-ASCII names.
+
+    Header values are encoded as latin-1, so a name outside that range cannot
+    go in filename at all. RFC 6266 handles this with a pair: a plain ASCII
+    filename for old clients, plus a percent-encoded UTF-8 filename* that
+    every current browser prefers.
+    """
+    ascii_name = filename if filename.isascii() else ascii_fallback
+
+    return (
+        f'attachment; filename="{ascii_name}"; '
+        f"filename*=UTF-8''{quote(filename, safe='')}"
+    )
+
+
 @router.get(
     "/cases/{case_id}/download",
     dependencies=[Depends(allow_any_authenticated())],
@@ -500,7 +512,9 @@ def download_export_case(
         _stream_case_archive(exports),
         media_type="application/zip",
         headers={
-            "Content-Disposition": f'attachment; filename="{archive_base}.zip"',
+            "Content-Disposition": _content_disposition(
+                f"{archive_base}.zip", f"{case_id}.zip"
+            ),
         },
     )
 

frigate/api/fastapi_app.py

@@ -35,6 +35,7 @@ from frigate.comms.event_metadata_updater import (
 )
 from frigate.config import FrigateConfig
 from frigate.config.camera.updater import CameraConfigUpdatePublisher
+from frigate.config.holder import ConfigHolder
 from frigate.config.profile_manager import ProfileManager
 from frigate.debug_replay import DebugReplayManager, debug_replay_auto_stop_watchdog
 from frigate.embeddings import EmbeddingsContext
@@ -74,6 +75,7 @@ def create_fastapi_app(
     dispatcher: Dispatcher | None = None,
     profile_manager: ProfileManager | None = None,
     enforce_default_admin: bool = True,
+    config_holder: ConfigHolder | None = None,
 ):
     logger.info("Starting FastAPI app")
     app = FastAPI(
@@ -150,6 +152,8 @@ def create_fastapi_app(
     app.include_router(debug_replay.router)
     # App Properties
     app.frigate_config = frigate_config
+    # snapshot the port nginx bound at startup, the live config can be swapped
+    app.auth_internal_port = frigate_config.networking.listen.internal_port
     app.genai_manager = GenAIClientManager(frigate_config)
     app.embeddings = embeddings
     app.detected_frames_processor = detected_frames_processor
@@ -162,6 +166,7 @@ def create_fastapi_app(
     app.replay_manager = replay_manager
     app.dispatcher = dispatcher
     app.profile_manager = profile_manager
+    app.config_holder = config_holder
 
     if frigate_config.auth.enabled:
         secret = get_jwt_secret()

frigate/api/media.py

@@ -53,6 +53,7 @@ from frigate.util.file import (
 )
 from frigate.util.image import get_image_from_recording, get_image_quality_params
 from frigate.util.media import get_keyframe_before
+from frigate.util.object import create_empty_regions_grid
 
 logger = logging.getLogger(__name__)
 
@@ -1083,7 +1084,21 @@ def clear_region_grid(request: Request, camera_name: str):
             status_code=404,
         )
 
-    Regions.delete().where(Regions.camera == camera_name).execute()
+    # store an empty grid instead of deleting the row so the grid is
+    # rebuilt from newly tracked objects and not from all past history
+    region = {
+        Regions.camera: camera_name,
+        Regions.grid: create_empty_regions_grid(),
+        Regions.last_update: datetime.now().timestamp(),
+    }
+    (
+        Regions.insert(region)
+        .on_conflict(
+            conflict_target=[Regions.camera],
+            update=region,
+        )
+        .execute()
+    )
     return JSONResponse(
         content={"success": True, "message": "Region grid cleared"},
     )

frigate/api/motion_search.py

@@ -182,7 +182,7 @@ async def get_motion_search_status_endpoint(
         )
 
     job = get_motion_search_job(job_id)
-    if not job:
+    if not job or job.camera != camera_name:
         return JSONResponse(
             content={"success": False, "message": "Job not found"},
             status_code=404,
@@ -253,7 +253,7 @@ async def cancel_motion_search_endpoint(
         )
 
     job = get_motion_search_job(job_id)
-    if not job:
+    if not job or job.camera != camera_name:
         return JSONResponse(
             content={"success": False, "message": "Job not found"},
             status_code=404,

frigate/api/notification.py

@@ -1,8 +1,10 @@
 """Notification apis."""
 
+import ipaddress
 import logging
 import os
 from typing import Any
+from urllib.parse import urlparse
 
 from cryptography.hazmat.primitives import serialization
 from fastapi import APIRouter, Depends, Request
@@ -19,6 +21,95 @@ logger = logging.getLogger(__name__)
 
 router = APIRouter(tags=[Tags.notifications])
 
+# Push endpoints are opaque URLs but stay well under this in practice
+MAX_ENDPOINT_LENGTH = 2048
+
+# Suffixes that only ever resolve on the local network
+INTERNAL_HOST_SUFFIXES = (".local", ".localdomain", ".internal", ".home.arpa")
+
+
+def _validate_push_endpoint(endpoint: Any) -> str | None:
+    """Return a reason the endpoint is unusable, or None when it is valid.
+
+    Subscriptions are issued by the browser vendor's push service, so a valid
+    endpoint is always a public https URL. Anything else is either a broken
+    registration or an attempt to aim the notification sender somewhere it
+    should not reach.
+    """
+    if not isinstance(endpoint, str) or not endpoint:
+        return "endpoint must be a url"
+
+    if len(endpoint) > MAX_ENDPOINT_LENGTH:
+        return "endpoint is too long"
+
+    try:
+        parsed = urlparse(endpoint)
+        port = parsed.port
+    except ValueError:
+        return "endpoint is not a valid url"
+
+    if parsed.scheme != "https":
+        return "endpoint must use https"
+
+    if parsed.username or parsed.password:
+        return "endpoint must not include credentials"
+
+    if port is not None and port != 443:
+        return "endpoint must use the default https port"
+
+    hostname = parsed.hostname
+
+    if not hostname:
+        return "endpoint must include a hostname"
+
+    try:
+        address = ipaddress.ip_address(hostname)
+    except ValueError:
+        address = None
+
+    if address is not None:
+        # A push service is never reachable at an address only this network can
+        # route, so anything non-global is a misconfiguration at best
+        if not address.is_global:
+            return "endpoint must not use a private address"
+    elif hostname == "localhost" or "." not in hostname:
+        return "endpoint must use a fully qualified hostname"
+    elif hostname.endswith(INTERNAL_HOST_SUFFIXES):
+        return "endpoint must not use an internal hostname"
+
+    # The subscription token lives in the path, and webpush.py assumes there is
+    # a separator after the host when it builds the VAPID audience
+    if len(parsed.path) <= 1:
+        return "endpoint must include a subscription path"
+
+    return None
+
+
+def _validate_subscription(sub: Any) -> str | None:
+    """Return a reason the subscription is unusable, or None when it is valid."""
+    if not isinstance(sub, dict):
+        return "subscription must be an object"
+
+    reason = _validate_push_endpoint(sub.get("endpoint"))
+
+    if reason:
+        return reason
+
+    keys = sub.get("keys")
+
+    if not isinstance(keys, dict):
+        return "subscription must include keys"
+
+    # WebPusher raises on a missing key, which would break every send for the
+    # user rather than just this registration
+    for name in ("p256dh", "auth"):
+        value = keys.get(name)
+
+        if not isinstance(value, str) or not value:
+            return f"subscription keys must include {name}"
+
+    return None
+
 
 @router.get(
     "/notifications/pubkey",
@@ -71,6 +162,17 @@ def register_notifications(request: Request, body: dict = None):
             status_code=400,
         )
 
+    reason = _validate_subscription(sub)
+
+    if reason:
+        logger.warning(
+            "Rejected notification registration for %s: %s", username, reason
+        )
+        return JSONResponse(
+            content={"success": False, "message": f"Invalid subscription: {reason}"},
+            status_code=400,
+        )
+
     try:
         User.update(notification_tokens=User.notification_tokens.append(sub)).where(
             User.username == username

frigate/api/review.py

@@ -17,6 +17,7 @@ from frigate.api.auth import (
     get_allowed_cameras_for_filter,
     get_current_user,
     require_camera_access,
+    require_full_camera_access,
     require_role,
 )
 from frigate.api.defs.query.review_query_parameters import (
@@ -709,6 +710,7 @@ async def get_review(request: Request, review_id: str):
     dependencies=[Depends(allow_any_authenticated())],
 )
 async def set_not_reviewed(
+    request: Request,
     review_id: str,
     current_user: dict = Depends(get_current_user),
 ):
@@ -727,6 +729,8 @@ async def set_not_reviewed(
             status_code=404,
         )
 
+    await require_camera_access(review.camera, request=request)
+
     try:
         user_review = UserReviewStatus.get(
             UserReviewStatus.user_id == user_id,
@@ -743,9 +747,12 @@ async def set_not_reviewed(
     )
 
 
+# Intentionally not camera scoped, as the summary correlates each flagged event
+# with overlapping activity on other cameras. Restricted to callers who can
+# already see every camera, so the unscoped query discloses nothing.
 @router.post(
     "/review/summarize/start/{start_ts}/end/{end_ts}",
-    dependencies=[Depends(require_role(["admin"]))],
+    dependencies=[Depends(require_full_camera_access)],
     description="Use GenAI to summarize review items over a period of time.",
 )
 def generate_review_summary(request: Request, start_ts: float, end_ts: float):

frigate/app.py

@@ -30,6 +30,7 @@ from frigate.comms.ws import WebSocketClient
 from frigate.comms.zmq_proxy import ZmqProxy
 from frigate.config.camera.updater import CameraConfigUpdatePublisher
 from frigate.config.config import FrigateConfig
+from frigate.config.holder import ConfigHolder
 from frigate.config.profile_manager import ProfileManager
 from frigate.const import (
     CACHE_DIR,
@@ -102,27 +103,25 @@ class FrigateApp:
         self.detection_shms: list[mp.shared_memory.SharedMemory] = []
         self.log_queue: Queue = mp.Queue()
         self.camera_metrics: DictProxy = self.metrics_manager.dict()
-        self.embeddings_metrics: DataProcessorMetrics | None = (
-            DataProcessorMetrics(
-                self.metrics_manager, list(config.classification.custom.keys())
-            )
-            if (
-                config.semantic_search.enabled
-                or any(
-                    c.objects.genai.enabled or c.review.genai.enabled
-                    for c in config.cameras.values()
-                )
-                or config.lpr.enabled
-                or config.face_recognition.enabled
-                or len(config.classification.custom) > 0
-            )
-            else None
+
+        self.embeddings_metrics = DataProcessorMetrics(
+            self.metrics_manager, list(config.classification.custom.keys())
         )
         self.ptz_metrics: dict[str, PTZMetrics] = {}
         self.processes: dict[str, int] = {}
         self.embeddings: EmbeddingsContext | None = None
-        self.profile_manager: ProfileManager | None = None
-        self.config = config
+        self.config_holder = ConfigHolder(config)
+
+    @property
+    def config(self) -> FrigateConfig:
+        """The current config, not the one Frigate booted with.
+
+        Read through the holder so the deferred watchdog factories below build
+        a replacement process from the config as it is now. There is no setter
+        on purpose: a plain attribute would let a caller pin this back to a
+        single object and reintroduce the staleness.
+        """
+        return self.config_holder.config
 
     def ensure_dirs(self) -> None:
         dirs = [
@@ -270,7 +269,7 @@ class FrigateApp:
                 10
                 * len([c for c in self.config.cameras.values() if c.enabled_in_config]),
             ),
-            load_vec_extension=self.config.semantic_search.enabled,
+            load_vec_extension=True,
         )
         models = [
             Event,
@@ -343,25 +342,6 @@ class FrigateApp:
         )
         self.dispatcher.profile_manager = self.profile_manager
 
-    def restore_active_profile(self) -> None:
-        """Re-activate the persisted profile after subscribers are connected.
-
-        ZMQ PUB/SUB drops messages with no subscribers, so activation must
-        run after every config_updater subscriber is up.
-        """
-        if self.profile_manager is None:
-            return
-
-        persisted = ProfileManager.load_persisted_profile()
-        if persisted and any(
-            persisted in cam.profiles for cam in self.config.cameras.values()
-        ):
-            logger.info("Restoring persisted profile '%s'", persisted)
-            # runtime overrides are layered on top via restore_runtime_state()
-            self.profile_manager.activate_profile(
-                persisted, clear_runtime_overrides=False
-            )
-
     def start_detectors(self) -> None:
         for name in self.config.cameras.keys():
             try:
@@ -610,6 +590,13 @@ class FrigateApp:
         self.start_detectors()
         self.init_dispatcher()
         self.init_profile_manager()
+
+        # workers get a copy of the config and can miss the broadcast below, so
+        # apply both layers here. must stay after init_profile_manager(), which
+        # snapshots the base config that profile deactivation resets to
+        self.profile_manager.restore_persisted_profile_to_config()
+        self.dispatcher.reapply_runtime_state_to_config()
+
         self.init_embeddings_client()
         self.start_video_output_processor()
         self.start_ptz_autotracker()
@@ -624,8 +611,9 @@ class FrigateApp:
         self.start_record_cleanup()
         self.start_watchdog()
 
-        # restore persisted runtime overrides on top of config
-        self.restore_active_profile()
+        # publish for the recording/review/embeddings processes, which start
+        # before the config can be corrected, and for the retained MQTT states
+        self.profile_manager.restore_persisted_profile()
         self.dispatcher.restore_runtime_state()
 
         self.init_auth()
@@ -645,6 +633,7 @@ class FrigateApp:
                     self.replay_manager,
                     self.dispatcher,
                     self.profile_manager,
+                    config_holder=self.config_holder,
                 ),
                 host="127.0.0.1",
                 port=5001,

frigate/camera/maintainer.py

@@ -117,7 +117,9 @@ class CameraMaintainer(threading.Thread):
 
         if runtime:
             self.camera_metrics[name] = CameraMetrics(self.metrics_manager)
-            self.ptz_metrics[name] = PTZMetrics(autotracker_enabled=False)
+            self.ptz_metrics[name] = PTZMetrics(
+                autotracker_enabled=config.onvif.autotracking.enabled
+            )
             self.region_grids[name] = get_camera_regions_grid(
                 name,
                 config.detect,

frigate/camera/state.py

@@ -60,6 +60,11 @@ class CameraState:
         # face/LPR pipelines when using a model without built-in detection.
         self.face_recognition_min_obj_area: int = 0
         self.lpr_min_obj_area: int = 0
+        self.lp_objects = {
+            label
+            for label, attributes in config.model.attributes_map.items()
+            if "license_plate" in attributes
+        }
 
         if (
             self.camera_config.face_recognition.enabled
@@ -111,9 +116,9 @@ class CameraState:
                 # draw thicker box around ptz autotracked object
                 if (
                     self.camera_config.onvif.autotracking.enabled
-                    and self.ptz_autotracker_thread.ptz_autotracker.autotracker_init[
+                    and self.ptz_autotracker_thread.ptz_autotracker.autotracker_init.get(
                         self.name
-                    ]
+                    )
                     and self.ptz_autotracker_thread.ptz_autotracker.tracked_object[
                         self.name
                     ]
@@ -452,7 +457,7 @@ class CameraState:
                 and obj_area >= self.face_recognition_min_obj_area
                 and updated_obj.obj_data.get("sub_label") is None
             ) or (
-                obj_label in ("car", "motorcycle")
+                obj_label in self.lp_objects
                 and self.lpr_min_obj_area > 0
                 and obj_area >= self.lpr_min_obj_area
                 and updated_obj.obj_data.get("sub_label") is None
@@ -548,7 +553,7 @@ class CameraState:
                     current_best.thumbnail_data is not None
                     and obj.thumbnail_data is not None
                     and is_better_thumbnail(
-                        object_type,
+                        obj.thumbnail_attributes,
                         current_best.thumbnail_data,
                         obj.thumbnail_data,
                         self.camera_config.frame_shape,

frigate/comms/dispatcher.py

@@ -404,38 +404,64 @@ class Dispatcher:
         for comm in self.comms:
             comm.stop()
 
-    def restore_runtime_state(self) -> None:
+    def apply_runtime_state(self) -> dict[str, dict[str, bool]]:
         """Replay persisted runtime overrides through the camera settings handlers.
 
-        Called once after Frigate startup completes so processing threads can
-        receive the resulting ``config_updater`` broadcasts. Unknown cameras
-        and topics are skipped; handler exceptions are logged and replay
-        continues for remaining entries.
+        Routing through the handlers (rather than mutating config directly) is
+        deliberate: they publish the ``config_updater`` broadcast and the
+        retained MQTT state as a side effect, so worker processes and the UI
+        converge on the replayed value. Unknown cameras and topics are skipped;
+        handler exceptions are logged and replay continues for the rest.
+
+        Returns:
+            The entries handed to a handler without raising, keyed by camera
+            then topic. A handler can still refuse the value internally (an ON
+            payload for a camera that is not enabled_in_config, for example),
+            so this is not proof the override took effect.
         """
         state = self._runtime_state.load()
+        applied: dict[str, dict[str, bool]] = {}
+
         for camera_name, features in state.items():
             if camera_name not in self.config.cameras:
                 continue
+
             for topic, value in features.items():
                 handler = self._camera_settings_handlers.get(topic)
+
                 if handler is None:
                     continue
+
                 payload = "ON" if value else "OFF"
+
                 try:
                     handler(camera_name, payload)
                 except Exception:
                     logger.exception(
-                        "Failed to restore runtime state %s.%s=%s",
+                        "Failed to apply runtime state %s.%s=%s",
                         camera_name,
                         topic,
                         payload,
                     )
                     continue
+
+                applied.setdefault(camera_name, {})[topic] = value
+
+        return applied
+
+    def restore_runtime_state(self) -> None:
+        """Replay persisted runtime overrides once Frigate startup completes.
+
+        Called after every ``config_updater`` subscriber is up so the resulting
+        broadcasts are not dropped by ZMQ PUB/SUB.
+        """
+        for camera_name, features in self.apply_runtime_state().items():
+            for topic, value in features.items():
                 logger.info(
                     "Restored runtime state: %s.%s=%s",
                     camera_name,
                     topic,
-                    payload,
+                    "ON" if value else "OFF",
                 )
 
     def clear_runtime_state_for_yaml_keys(self, dotted_keys: Iterable[str]) -> None:
@@ -458,6 +484,56 @@ class Dispatcher:
         """
         self._runtime_state.clear_all()
 
+    def clear_runtime_state_for_camera(self, camera: str) -> None:
+        """Drop all persisted runtime overrides for a deleted camera.
+
+        Called by camera deletion so a camera later added under the same name
+        does not inherit the removed camera's stale toggles.
+        """
+        self._runtime_state.clear_camera(camera)
+
+    def reapply_runtime_state_to_config(self) -> None:
+        """Re-apply persisted runtime overrides to the swapped-in config object.
+
+        After config/set (or a camera delete) parses fresh yaml and swaps the
+        config, the worker processes still hold the live toggle values and the
+        overrides are already on disk, so only the in-process config object is
+        out of date. Unlike apply_runtime_state (used at startup, where workers
+        must be told), this makes no ZMQ, MQTT, or disk writes, it just corrects
+        the config the API and dispatcher read.
+
+        The field mutations and gates mirror the _on_*_command handlers; keep
+        the two in sync if a tracked toggle is added or its gate changes.
+        """
+        state = self._runtime_state.load()
+
+        for camera_name, features in state.items():
+            camera = self.config.cameras.get(camera_name)
+
+            if camera is None:
+                continue
+
+            for topic, value in features.items():
+                if topic == "enabled":
+                    if value and not camera.enabled_in_config:
+                        continue
+                    camera.enabled = value
+                elif topic == "detect":
+                    camera.detect.enabled = value
+                    # detection requires motion, mirror the handler coupling
+                    if value and not camera.motion.enabled:
+                        camera.motion.enabled = True
+                elif topic == "snapshots":
+                    camera.snapshots.enabled = value
+                elif topic == "recordings":
+                    if value and not camera.record.enabled_in_config:
+                        continue
+                    camera.record.enabled = value
+                elif topic == "audio":
+                    if value and not camera.audio.enabled_in_config:
+                        continue
+                    camera.audio.enabled = value
+
     def _on_detect_command(self, camera_name: str, payload: str) -> None:
         """Callback for detect topic."""
         detect_settings = self.config.cameras[camera_name].detect
@@ -588,6 +664,10 @@ class Dispatcher:
                 self.ptz_metrics[camera_name].start_time.value = 0
                 ptz_autotracker_settings.enabled = False
 
+        self.config_updater.publish_update(
+            CameraConfigUpdateTopic(CameraConfigUpdateEnum.autotracking, camera_name),
+            ptz_autotracker_settings,
+        )
         self.publish(f"{camera_name}/ptz_autotracker/state", payload, retain=True)
 
     def _on_motion_contour_area_command(self, camera_name: str, payload: int) -> None:

frigate/comms/mqtt.py

@@ -77,6 +77,11 @@ class MqttClient(Communicator):
                 "ON" if camera.audio.enabled_in_config else "OFF",
                 retain=True,
             )
+            self.publish(
+                f"{camera_name}/audio_transcription/state",
+                "ON" if camera.audio_transcription.live_enabled else "OFF",
+                retain=True,
+            )
             self.publish(
                 f"{camera_name}/detect/state",
                 "ON" if camera.detect.enabled else "OFF",
@@ -258,6 +263,7 @@ class MqttClient(Communicator):
             "snapshots",
             "detect",
             "audio",
+            "audio_transcription",
             "motion",
             "improve_contrast",
             "ptz_autotracker",

frigate/comms/runtime_state.py

@@ -96,6 +96,25 @@ class RuntimeStatePersistence:
         except OSError:
             logger.exception("Failed to clear runtime state")
 
+    def clear_camera(self, camera: str) -> None:
+        """Drop every stored override for a single camera.
+
+        Called when a camera is deleted so a camera later added under the same
+        name does not inherit the removed camera's stale toggles.
+        """
+        try:
+            with FileLock(self._lock_path, timeout=self._lock_timeout):
+                data = self._read_locked()
+                cameras = data.get("cameras")
+                if not isinstance(cameras, dict) or camera not in cameras:
+                    return
+                del cameras[camera]
+                self._write_locked(data)
+        except Timeout:
+            logger.error("Timed out clearing runtime state for camera")
+        except OSError:
+            logger.exception("Failed to clear runtime state for camera")
+
     def clear_for_yaml_keys(self, dotted_keys: Iterable[str]) -> None:
         """Remove stored entries whose YAML key was just rewritten.
 

frigate/comms/webpush.py

@@ -89,7 +89,9 @@ class WebPushClient(Communicator):
         # notification and auth config updater
         self.global_config_subscriber = ConfigSubscriber("config/")
         self.config_subscriber = CameraConfigUpdateSubscriber(
-            self.config, self.config.cameras, [CameraConfigUpdateEnum.notifications]
+            self.config,
+            self.config.cameras,
+            [CameraConfigUpdateEnum.add, CameraConfigUpdateEnum.notifications],
         )
         self._refresh_user_cameras()
 
@@ -213,6 +215,8 @@ class WebPushClient(Communicator):
                 self.suspended_cameras[camera] = 0
                 self.last_camera_notification_time[camera] = 0
 
+            self._refresh_user_cameras()
+
         if topic == "reviews":
             decoded = json.loads(payload)
             camera = decoded["before"]["camera"]
@@ -417,6 +421,7 @@ class WebPushClient(Communicator):
         # Don't notify if message is an update and important fields don't have an update
         if (
             state == "update"
+            and payload["before"]["severity"] == payload["after"]["severity"]
             and len(payload["before"]["data"]["objects"])
             == len(payload["after"]["data"]["objects"])
             and len(payload["before"]["data"]["zones"])

frigate/comms/ws.py

@@ -23,7 +23,6 @@ from frigate.const import (
     EXPIRE_AUDIO_ACTIVITY,
     INSERT_MANY_RECORDINGS,
     INSERT_PREVIEW,
-    NOTIFICATION_TEST,
     REQUEST_REGION_GRID,
     UPDATE_AUDIO_ACTIVITY,
     UPDATE_AUDIO_TRANSCRIPTION_STATE,
@@ -57,7 +56,6 @@ _WS_BLOCKED_TOPICS = frozenset(
         UPDATE_EMBEDDINGS_REINDEX_PROGRESS,
         UPDATE_BIRDSEYE_LAYOUT,
         UPDATE_AUDIO_TRANSCRIPTION_STATE,
-        NOTIFICATION_TEST,
     }
 )
 

frigate/config/camera/ui.py

@@ -13,8 +13,8 @@ class CameraUiConfig(FrigateBaseModel):
     )
     dashboard: bool = Field(
         default=True,
-        title="Show in UI",
-        description="Toggle whether this camera is visible everywhere in the Frigate UI. Disabling this will require manually editing the config to view this camera in the UI again.",
+        title="Show on Live dashboard",
+        description="Toggle whether this camera is visible on the default All Cameras live dashboard. The camera remains available everywhere else in the UI, including camera groups and settings.",
     )
     review: bool = Field(
         default=True,

frigate/config/camera/updater.py

@@ -14,6 +14,7 @@ class CameraConfigUpdateEnum(str, Enum):
     add = "add"  # for adding a camera
     audio = "audio"
     audio_transcription = "audio_transcription"
+    autotracking = "autotracking"  # ptz autotracking only, without an onvif reinit
     birdseye = "birdseye"
     detect = "detect"
     enabled = "enabled"
@@ -145,6 +146,8 @@ class CameraConfigUpdateSubscriber:
             config.snapshots = updated_config
         elif update_type == CameraConfigUpdateEnum.onvif:
             config.onvif = updated_config
+        elif update_type == CameraConfigUpdateEnum.autotracking:
+            config.onvif.autotracking = updated_config
         elif update_type == CameraConfigUpdateEnum.timestamp_style:
             config.timestamp_style = updated_config
         elif update_type == CameraConfigUpdateEnum.zones:

frigate/config/config.py

@@ -400,6 +400,9 @@ def verify_objects_track(
         )
         camera_config.objects.track = valid_objects
 
+        for label in invalid_objects:
+            camera_config.objects.filters.pop(label, None)
+
 
 def verify_lpr_and_face(
     frigate_config: FrigateConfig, camera_config: CameraConfig
@@ -637,7 +640,7 @@ class FrigateConfig(FrigateBaseModel):
         # set notifications state
         self.notifications.enabled_in_config = self.notifications.enabled
 
-        # validate genai: each role (tools, vision, embeddings) at most once
+        # validate genai: each role (chat, descriptions, embeddings) at most once
         role_to_name: dict[GenAIRoleEnum, str] = {}
         for name, genai_cfg in self.genai.items():
             for role in genai_cfg.roles:

frigate/config/holder.py

@@ -0,0 +1,34 @@
+"""Shared handle on the config object that is current for this instance."""
+
+from .config import FrigateConfig
+
+__all__ = ["ConfigHolder"]
+
+
+class ConfigHolder:
+    """Indirection for the most recently parsed config.
+
+    /api/config/set re-parses yaml into a brand new FrigateConfig instead of
+    mutating the old one, so any reference captured during startup goes stale
+    the first time a user saves. Anything that has to build something after
+    startup, most importantly the watchdog factories that rebuild a crashed
+    process, must read through a holder rather than close over a config
+    object, or the rebuilt process comes back with the config as it was at
+    boot and silently discards every change made since.
+
+    There is deliberately no setter on the read side: the swap runs in exactly
+    one place (frigate.api.config_util.swap_runtime_config) and everyone else
+    only reads.
+    """
+
+    def __init__(self, config: FrigateConfig) -> None:
+        self._config = config
+
+    @property
+    def config(self) -> FrigateConfig:
+        """The config as of the most recent successful save."""
+        return self._config
+
+    def set(self, config: FrigateConfig) -> None:
+        """Install a freshly parsed config as the current one."""
+        self._config = config

frigate/config/network.py

@@ -1,10 +1,18 @@
-from pydantic import Field
+from pydantic import Field, model_validator
 
 from .base import FrigateBaseModel
 
 __all__ = ["IPv6Config", "ListenConfig", "NetworkingConfig"]
 
 
+def parse_listen_port(value: int | str) -> int:
+    """Return the port number from a bare port or an "address:port" value."""
+    if isinstance(value, str):
+        return int(value.split(":")[-1])
+
+    return value
+
+
 class IPv6Config(FrigateBaseModel):
     enabled: bool = Field(
         default=False,
@@ -25,6 +33,21 @@ class ListenConfig(FrigateBaseModel):
         description="External listening port for Frigate (default 8971).",
     )
 
+    @property
+    def internal_port(self) -> int:
+        return parse_listen_port(self.internal)
+
+    @property
+    def external_port(self) -> int:
+        return parse_listen_port(self.external)
+
+    @model_validator(mode="after")
+    def validate_distinct_ports(self) -> "ListenConfig":
+        if self.internal_port == self.external_port:
+            raise ValueError("internal and external must listen on different ports")
+
+        return self
+
 
 class NetworkingConfig(FrigateBaseModel):
     ipv6: IPv6Config = Field(

frigate/config/profile_manager.py

@@ -141,6 +141,11 @@ class ProfileManager:
         Preserves active profile state: re-snapshots base configs from the new
         (freshly parsed) config, then re-applies profile overrides if a profile
         was active.
+
+        Deliberately does not clear the dispatcher's runtime overrides. This is
+        the config-save path, not a profile switch: the save only invalidates
+        the toggles it rewrote in yaml, which /api/config/set already clears by
+        key. The broad wipe belongs to activate_profile alone.
         """
         current_active = self.config.active_profile
         self.config = new_config
@@ -164,9 +169,92 @@ class ProfileManager:
                 self.config.active_profile = None
                 self._persist_active_profile(None)
 
-            # drop all runtime overrides so they don't replay stale values on restart
-            if self.dispatcher is not None:
-                self.dispatcher.clear_runtime_state()
+    def _validate_profile_name(self, profile_name: str | None) -> str | None:
+        """Return an error message if the name is not a defined profile."""
+        if profile_name is not None and profile_name not in self.config.profiles:
+            return f"Profile '{profile_name}' is not defined in the profiles section"
+
+        return None
+
+    def _apply_to_config(
+        self, profile_name: str | None
+    ) -> tuple[dict[str, set[str]], str | None]:
+        """Reset every camera to base, then apply the named profile on top.
+
+        Returns the changed camera/section pairs, plus an error message if
+        applying the profile failed partway through.
+        """
+        changed: dict[str, set[str]] = {}
+
+        self._reset_to_base(changed)
+
+        if profile_name is not None:
+            err = self._apply_profile_overrides(profile_name, changed)
+            if err:
+                return changed, err
+
+        return changed, None
+
+    def apply_profile_to_config(self, profile_name: str | None) -> str | None:
+        """Apply a profile to the in-memory config, without publishing it.
+
+        Safe to call ahead of activate_profile: both reset to the base config
+        first, so the later call re-derives the same state and still reports
+        every section as changed.
+
+        Returns:
+            None on success, or an error message string on failure.
+        """
+        err = self._validate_profile_name(profile_name)
+
+        if err:
+            return err
+
+        return self._apply_to_config(profile_name)[1]
+
+    def _persisted_profile_to_restore(self) -> str | None:
+        """Return the persisted profile name, if it still applies to a camera."""
+        persisted = self.load_persisted_profile()
+
+        if not persisted or not any(
+            persisted in cam.profiles for cam in self.config.cameras.values()
+        ):
+            return None
+
+        return persisted
+
+    def restore_persisted_profile_to_config(self) -> None:
+        """Restore the persisted profile into the config, without publishing.
+
+        Called before worker processes start, so they are handed a config that
+        already carries the profile rather than relying on the broadcast that
+        restore_persisted_profile() sends later.
+        """
+        persisted = self._persisted_profile_to_restore()
+
+        if persisted is None:
+            return
+
+        err = self.apply_profile_to_config(persisted)
+
+        if err:
+            logger.error("Failed to apply persisted profile '%s': %s", persisted, err)
+
+    def restore_persisted_profile(self) -> None:
+        """Re-activate the persisted profile once subscribers are connected.
+
+        The config already carries the profile; this pass publishes it for the
+        processes that start before the config can be corrected, and for the
+        retained MQTT states.
+        """
+        persisted = self._persisted_profile_to_restore()
+
+        if persisted is None:
+            return
+
+        logger.info("Restoring persisted profile '%s'", persisted)
+        # runtime overrides are layered on top by the dispatcher's replay
+        self.activate_profile(persisted, clear_runtime_overrides=False)
 
     def activate_profile(
         self,
@@ -186,23 +274,16 @@ class ProfileManager:
         Returns:
             None on success, or an error message string on failure.
         """
-        if profile_name is not None:
-            if profile_name not in self.config.profiles:
-                return (
-                    f"Profile '{profile_name}' is not defined in the profiles section"
-                )
+        err = self._validate_profile_name(profile_name)
+
+        if err:
+            return err
 
         # Track which camera/section pairs get changed for ZMQ publishing
-        changed: dict[str, set[str]] = {}
+        changed, err = self._apply_to_config(profile_name)
 
-        # Reset all cameras to base config
-        self._reset_to_base(changed)
-
-        # Apply new profile overrides if activating
-        if profile_name is not None:
-            err = self._apply_profile_overrides(profile_name, changed)
-            if err:
-                return err
+        if err:
+            return err
 
         # Publish ZMQ updates only for sections that actually changed
         self._publish_updates(changed)

frigate/const.py

@@ -44,7 +44,11 @@ DEFAULT_ATTRIBUTE_LABEL_MAP = {
         "ups",
         "usps",
     ],
+    "truck": ["license_plate"],
+    "garbage_truck": ["license_plate"],
     "motorcycle": ["license_plate"],
+    "bus": ["license_plate"],
+    "school_bus": ["license_plate"],
 }
 ATTRIBUTE_LABEL_DISPLAY_MAP = {
     "amazon": "Amazon",

frigate/data_processing/common/license_plate/mixin.py

@@ -1172,6 +1172,28 @@ class LicensePlateProcessingMixin:
 
         return rep["plate"], rep["conf"], rep["char_confidences"], rep["area"]
 
+    def _passes_plate_filters(self, camera: str, plate: str) -> bool:
+        """Check a plate against the configured length and format filters."""
+        if len(plate) < self.lpr_config.min_plate_length:
+            logger.debug(
+                f"{camera}: Filtered out plate '{plate}' due to length ({len(plate)} < {self.lpr_config.min_plate_length})"
+            )
+            return False
+
+        if self.lpr_config.format:
+            try:
+                if not re.fullmatch(self.lpr_config.format, plate):
+                    logger.debug(
+                        f"{camera}: Filtered out plate '{plate}' due to format mismatch"
+                    )
+                    return False
+            except re.error:
+                logger.error(
+                    f"{camera}: Invalid regex in LPR format configuration: {self.lpr_config.format}"
+                )
+
+        return True
+
     def _generate_plate_event(self, camera: str, plate: str, plate_score: float) -> str:
         """Generate a unique ID for a plate event based on camera and text."""
         now = datetime.datetime.now().timestamp()
@@ -1268,7 +1290,7 @@ class LicensePlateProcessingMixin:
                 and obj_data.get("label") != "license_plate"
             ):
                 logger.debug(
-                    f"{camera}: Not a processing license plate for non car/motorcycle object."
+                    f"{camera}: Not a processing license plate for {obj_data.get('label', 'unknown')}."
                 )
                 return
 
@@ -1345,7 +1367,7 @@ class LicensePlateProcessingMixin:
 
                 if not license_plate:
                     logger.debug(
-                        f"{camera}: Detected no license plates for car/motorcycle object."
+                        f"{camera}: Detected no license plates for {obj_data.get('label', 'unknown')} object."
                     )
                     return
 
@@ -1511,10 +1533,14 @@ class LicensePlateProcessingMixin:
             plate_id = None
 
             for existing_id, data in self.detected_license_plates.items():
+                # entries from the object pipeline on this camera have no
+                # last_seen until they pass the filters below
+                last_seen = data.get("last_seen")
+
                 if (
                     data["camera"] == camera
-                    and data["last_seen"] is not None
-                    and current_time - data["last_seen"]
+                    and last_seen is not None
+                    and current_time - last_seen
                     <= self.config.cameras[camera].lpr.expire_time
                 ):
                     similarity = JaroWinkler.similarity(data["plate"], top_plate)
@@ -1525,6 +1551,11 @@ class LicensePlateProcessingMixin:
                         )
                         break
             if plate_id is None:
+                # the event id doubles as the cluster key, so a plate rejected
+                # after this point would leave an entry that never expires
+                if not self._passes_plate_filters(camera, top_plate):
+                    return
+
                 plate_id = self._generate_plate_event(camera, top_plate, avg_confidence)
                 logger.debug(
                     f"{camera}: New plate event for dedicated LPR camera {plate_id}: {top_plate}"
@@ -1569,27 +1600,12 @@ class LicensePlateProcessingMixin:
                 f"{camera}: Clustering changed top plate '{top_plate}' (conf: {avg_confidence:.3f}) to rep '{rep_plate}' (conf: {rep_conf:.3f})"
             )
 
-        # Apply length and format filters to the clustered representative
-        # rather than individual OCR readings, so noisy variants still
-        # contribute to clustering even when they don't pass on their own.
-        if len(rep_plate) < self.lpr_config.min_plate_length:
-            logger.debug(
-                f"{camera}: Filtered out clustered plate '{rep_plate}' due to length ({len(rep_plate)} < {self.lpr_config.min_plate_length})"
-            )
+        # filter the clustered representative rather than individual OCR
+        # readings, so noisy variants still contribute to clustering even
+        # when they don't pass on their own
+        if not self._passes_plate_filters(camera, rep_plate):
             return
 
-        if self.lpr_config.format:
-            try:
-                if not re.fullmatch(self.lpr_config.format, rep_plate):
-                    logger.debug(
-                        f"{camera}: Filtered out clustered plate '{rep_plate}' due to format mismatch"
-                    )
-                    return
-            except re.error:
-                logger.error(
-                    f"{camera}: Invalid regex in LPR format configuration: {self.lpr_config.format}"
-                )
-
         # Update stored rep
         self.detected_license_plates[id].update(
             {

frigate/data_processing/post/object_descriptions.py

@@ -63,8 +63,10 @@ class ObjectDescriptionProcessor(PostProcessorApi):
         """Handle an update to a frame for an object."""
         camera_config = self.config.cameras[camera]
 
-        # no need to save our own thumbnails if genai is not enabled
-        # or if the object has become stationary
+        if not camera_config.objects.genai.enabled:
+            return
+
+        # no need to save our own thumbnails if the object has become stationary
         if not data["stationary"]:
             if data["id"] not in self.tracked_events:
                 self.tracked_events[data["id"]] = []

frigate/data_processing/real_time/face.py

@@ -28,6 +28,7 @@ from frigate.data_processing.common.face.model import (
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed
 from frigate.util.image import area
+from frigate.util.path import safe_join, sanitize_path_component
 
 from ..types import DataProcessorMetrics
 from .api import RealTimeProcessorApi
@@ -288,6 +289,10 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 max(0, face_box[0]) : min(frame.shape[1], face_box[2]),
             ]
 
+        if face_frame.size == 0:
+            logger.debug(f"Empty face crop for {id}")
+            return
+
         res = self.recognizer.classify(face_frame)
 
         if not res:
@@ -405,9 +410,17 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                 )
 
             # write face to library
-            folder = os.path.join(FACE_DIR, label)
+            sanitized_label = sanitize_path_component(label)
+            folder = safe_join(FACE_DIR, label)
+
+            if sanitized_label is None or folder is None:
+                return {
+                    "message": f"Invalid face name: {label}",
+                    "success": False,
+                }
+
             file = os.path.join(
-                folder, f"{label}_{datetime.datetime.now().timestamp()}.webp"
+                folder, f"{sanitized_label}_{datetime.datetime.now().timestamp()}.webp"
             )
             os.makedirs(folder, exist_ok=True)
 

frigate/db/sqlitevecq.py

@@ -1,9 +1,14 @@
-import re
+import logging
 import sqlite3
 from typing import Any
 
+import regex
 from playhouse.sqliteq import SqliteQueueDatabase
 
+logger = logging.getLogger(__name__)
+
+REGEXP_TIMEOUT_SECONDS = 1.0
+
 
 class SqliteVecQueueDatabase(SqliteQueueDatabase):
     def __init__(
@@ -26,27 +31,55 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
 
     def _load_vec_extension(self, conn: sqlite3.Connection) -> None:
         conn.enable_load_extension(True)
-        conn.load_extension(self.sqlite_vec_path)
-        conn.enable_load_extension(False)
+
+        try:
+            conn.load_extension(self.sqlite_vec_path)
+        except conn.OperationalError:
+            logger.error("Unable to load the sqlite-vec extension")
+            self.load_vec_extension = False
+        finally:
+            conn.enable_load_extension(False)
 
     def _register_regexp(self, conn: sqlite3.Connection) -> None:
         def regexp(expr: str, item: str | None) -> bool:
             if item is None:
                 return False
             try:
-                return re.search(expr, item) is not None
-            except re.error:
+                return (
+                    regex.search(expr, item, timeout=REGEXP_TIMEOUT_SECONDS) is not None
+                )
+            except (regex.error, TimeoutError):
                 return False
 
         conn.create_function("REGEXP", 2, regexp)
 
-    def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
+    def _delete_embeddings(self, table: str, event_ids: list[str]) -> None:
+        """Delete embeddings for the given events, if the table exists.
+
+        Embeddings outlive the events they belong to when semantic search is
+        disabled, so deletes are attempted regardless of the current config.
+        """
+        if not event_ids or not self.load_vec_extension:
+            return
+
+        # the embeddings tables are only created once semantic search has run
+        cursor = self.execute_sql(
+            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
+            (table,),
+        )
+
+        if cursor.fetchone() is None:
+            logger.debug("Skipping %s cleanup, table does not exist", table)
+            return
+
         ids = ",".join(["?" for _ in event_ids])
-        self.execute_sql(f"DELETE FROM vec_thumbnails WHERE id IN ({ids})", event_ids)
+        self.execute_sql(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
+
+    def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
+        self._delete_embeddings("vec_thumbnails", event_ids)
 
     def delete_embeddings_description(self, event_ids: list[str]) -> None:
-        ids = ",".join(["?" for _ in event_ids])
-        self.execute_sql(f"DELETE FROM vec_descriptions WHERE id IN ({ids})", event_ids)
+        self._delete_embeddings("vec_descriptions", event_ids)
 
     def drop_embeddings_tables(self) -> None:
         self.execute_sql("""

frigate/debug_replay.py

@@ -21,8 +21,6 @@ from frigate.config.camera.updater import (
     CameraConfigUpdateTopic,
 )
 from frigate.const import (
-    CLIPS_DIR,
-    RECORD_DIR,
     REPLAY_CAMERA_PREFIX,
     REPLAY_DIR,
     THUMB_DIR,
@@ -331,12 +329,14 @@ def cleanup_replay_cameras() -> None:
     """
     stale_cameras: set[str] = set()
 
-    # Scan filesystem for leftover replay artifacts to derive camera names
-    for dir_path in [RECORD_DIR, CLIPS_DIR, THUMB_DIR]:
-        if os.path.isdir(dir_path):
-            for entry in os.listdir(dir_path):
-                if entry.startswith(REPLAY_CAMERA_PREFIX):
-                    stale_cameras.add(entry)
+    # Derive stale camera names from THUMB_DIR (per-camera dirs) and
+    # REPLAY_DIR (the session's source clip); both listings are bounded by
+    # camera count. cleanup_camera_files below removes any remaining
+    # per-camera artifacts (snapshots, thumbnails, LPR images, etc.) by name.
+    if os.path.isdir(THUMB_DIR):
+        for entry in os.listdir(THUMB_DIR):
+            if entry.startswith(REPLAY_CAMERA_PREFIX):
+                stale_cameras.add(entry)
 
     if os.path.isdir(REPLAY_DIR):
         for entry in os.listdir(REPLAY_DIR):

frigate/detectors/detection_runners.py

@@ -25,25 +25,31 @@ def is_arm64_platform() -> bool:
     return machine in ("aarch64", "arm64", "armv8", "armv7l")
 
 
-def get_ort_session_options(
-    is_complex_model: bool = False,
-) -> ort.SessionOptions | None:
+def get_ort_session_options(model_type: str | None = None) -> ort.SessionOptions | None:
     """Get ONNX Runtime session options with appropriate settings.
 
     Args:
-        is_complex_model: Whether the model needs basic optimization to avoid graph fusion issues.
+        model_type: Model being loaded, used to pin its graph optimization level.
 
     Returns:
-        SessionOptions with appropriate optimization level, or None for default settings.
+        SessionOptions with a pinned optimization level, or None for default settings.
     """
-    if is_complex_model:
-        sess_options = ort.SessionOptions()
-        sess_options.graph_optimization_level = (
-            ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
-        )
-        return sess_options
+    # Import here to avoid circular imports
+    from frigate.embeddings.types import EnrichmentModelTypeEnum
 
-    return None
+    if model_type == EnrichmentModelTypeEnum.jina_v2.value:
+        # below EXTENDED the CUDA EP returns an identical vector for every image,
+        # and ORT_ENABLE_ALL fails to build on CPU with a SimplifiedLayerNormFusion error
+        level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
+    elif model_type == EnrichmentModelTypeEnum.jina_v1.value:
+        # aggressive optimizations create or expect nodes that don't exist
+        level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
+    else:
+        return None
+
+    sess_options = ort.SessionOptions()
+    sess_options.graph_optimization_level = level
+    return sess_options
 
 
 # Import OpenVINO only when needed to avoid circular dependencies
@@ -115,21 +121,6 @@ class BaseModelRunner(ABC):
 class ONNXModelRunner(BaseModelRunner):
     """Run ONNX models using ONNX Runtime."""
 
-    @staticmethod
-    def is_cpu_complex_model(model_type: str) -> bool:
-        """Check if model needs basic optimization level to avoid graph fusion issues.
-
-        Some models (like Jina-CLIP) have issues with aggressive optimizations like
-        SimplifiedLayerNormFusion that create or expect nodes that don't exist.
-        """
-        # Import here to avoid circular imports
-        from frigate.embeddings.types import EnrichmentModelTypeEnum
-
-        return model_type in [
-            EnrichmentModelTypeEnum.jina_v1.value,
-            EnrichmentModelTypeEnum.jina_v2.value,
-        ]
-
     @staticmethod
     def is_migraphx_complex_model(model_type: str) -> bool:
         # Import here to avoid circular imports
@@ -323,6 +314,12 @@ class OpenVINOModelRunner(BaseModelRunner):
         if device in ["GPU", "AUTO", "NPU"]:
             self.ov_core.set_property(device, {"PERFORMANCE_HINT": "LATENCY"})
 
+        if device in ["GPU", "AUTO"]:
+            try:
+                self.ov_core.set_property("GPU", {"GPU_QUEUE_THROTTLE": "LOW"})
+            except Exception as e:
+                logger.debug(f"GPU_QUEUE_THROTTLE not supported: {e}")
+
         if device == "NPU" and OpenVINOModelRunner.is_detection_model(model_type):
             try:
                 self.ov_core.set_property(device, {"NPU_TURBO": "YES"})
@@ -626,9 +623,7 @@ def get_optimized_runner(
     return ONNXModelRunner(
         ort.InferenceSession(
             model_path,
-            sess_options=get_ort_session_options(
-                ONNXModelRunner.is_cpu_complex_model(model_type)
-            ),
+            sess_options=get_ort_session_options(model_type),
             providers=providers,
             provider_options=options,
         ),

frigate/detectors/detector_config.py

@@ -93,7 +93,7 @@ class ModelConfig(BaseModel):
     model_type: ModelTypeEnum = Field(
         default=ModelTypeEnum.ssd,
         title="Object Detection Model Type",
-        description="Detector model architecture type (ssd, yolox, yolonas) used by some detectors for optimization.",
+        description="Detector model architecture type (ssd, yolox, yolonas, yolo-generic, rfdetr, dfine) used by some detectors for optimization.",
     )
     _merged_labelmap: dict[int, str] | None = PrivateAttr()
     _colormap: dict[int, tuple[int, int, int]] = PrivateAttr()