docs/docs/configuration/face_recognition.md

@@ -9,7 +9,7 @@ Face recognition identifies known individuals by matching detected faces with pr
 
 ### Face Detection
 
-When running a Frigate+ model (or any custom model that natively detects faces) should ensure that `face` is added to the [list of objects to track](../plus/index.md#available-label-types) either globally or for a specific camera. This will allow face detection to run at the same time as object detection and be more efficient.
+When running a Frigate+ model (or any custom model that natively detects faces) should ensure that `face` is added to the [list of objects to track](../plus/#available-label-types) either globally or for a specific camera. This will allow face detection to run at the same time as object detection and be more efficient.
 
 When running a default COCO model or another model that does not include `face` as a detectable label, face detection will run via CV2 using a lightweight DNN model that runs on the CPU. In this case, you should _not_ define `face` in your list of objects to track.
 

docs/docs/configuration/object_detectors.md

@@ -1110,7 +1110,17 @@ model:
 
 #### Using a Custom Model
 
-To use your own custom model, first compile it into a [.dfp](https://developer.memryx.com/2p1/specs/files.html#dataflow-program) file, which is the format used by MemryX.
+To use your own model:
+
+1.  Package your compiled model into a `.zip` file.
+
+2.  The `.zip` must contain the compiled `.dfp` file.
+
+3.  Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
+
+4.  Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
+
+5.  Update the `labelmap_path` to match your custom model's labels.
 
 #### Compile the Model
 
@@ -1119,31 +1129,18 @@ Custom models must be compiled using **MemryX SDK 2.1**.
 Before compiling your model, install the MemryX Neural Compiler tools from the
 [Install Tools](https://developer.memryx.com/2p1/get_started/install_tools.html) page on the **host**.
 
-> **Note:** It is recommended to compile the model on the host machine, or on another separate machine, rather than inside the Frigate Docker container. Installing the compiler inside Docker may conflict with container packages. It is recommended to create a Python virtual environment and install the compiler there.
-
 Once the SDK 2.1 environment is set up, follow the
 [MemryX Compiler](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage) documentation to compile your model.
 
 Example:
 
 ```bash
-mx_nc -m yolonas.onnx -c 4 --autocrop -v --dfp_fname yolonas.dfp
+mx_nc -m ./yolov9.onnx --dfp_fname ./yolov9.dfp -is "1,3,640,640" -c 4 --autocrop -v
 ```
+> **Note:** `-is` specifies the input shape. Use your model's input dimensions.
 
 For detailed instructions on compiling models, refer to the [MemryX Compiler](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage) docs and [Tutorials](https://developer.memryx.com/2p1/tutorials/tutorials.html).
 
-#### Package the Compiled Model
-
-1. Package your compiled model into a `.zip` file.
-
-2. The `.zip` file must contain the compiled `.dfp` file.
-
-3. Depending on the model, the compiler may also generate a cropped post-processing network. If present, it will be named with the suffix `_post.onnx`.
-
-4. Bind-mount the `.zip` file into the container and specify its path using `model.path` in your config.
-
-5. Update `labelmap_path` to match your custom model's labels.
-
 ```yaml
 # The detector automatically selects the default model if nothing is provided in the config.
 #

docs/docs/configuration/record.md

@@ -68,7 +68,7 @@ record:
 
 ## Will Frigate delete old recordings if my storage runs out?
 
-If there is less than an hour left of storage, the oldest hour of recordings will be deleted and a message will be printed in the Frigate logs. This emergency cleanup deletes the oldest recordings first regardless of retention settings to reclaim space as quickly as possible.
+As of Frigate 0.12 if there is less than an hour left of storage, the oldest 2 hours of recordings will be deleted.
 
 ## Configuring Recording Retention
 

docs/docs/configuration/restream.md

@@ -208,7 +208,7 @@ Enabling arbitrary exec sources allows execution of arbitrary commands through g
 
 ## Advanced Restream Configurations
 
-The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#source-exec) source in go2rtc can be used for custom ffmpeg commands and other applications. An example is below:
+The [exec](https://github.com/AlexxIT/go2rtc/tree/v1.9.10#source-exec) source in go2rtc can be used for custom ffmpeg commands. An example is below:
 
 :::warning
 
@@ -216,11 +216,16 @@ The `exec:`, `echo:`, and `expr:` sources are disabled by default for security.
 
 :::
 
-NOTE: RTSP output will need to be passed with two curly braces `{{output}}`, whereas pipe output must be passed without curly braces.
+:::warning
+
+The `exec:`, `echo:`, and `expr:` sources are disabled by default for security. You must set `GO2RTC_ALLOW_ARBITRARY_EXEC=true` to use them. See [Security: Restricted Stream Sources](#security-restricted-stream-sources) for more information.
+
+:::
+
+NOTE: The output will need to be passed with two curly braces `{{output}}`
 
 ```yaml
 go2rtc:
   streams:
     stream1: exec:ffmpeg -hide_banner -re -stream_loop -1 -i /media/BigBuckBunny.mp4 -c copy -rtsp_transport tcp -f rtsp {{output}}
-    stream2: exec:rpicam-vid -t 0 --libav-format h264 -o -
 ```

docs/docs/frigate/installation.md

@@ -7,7 +7,7 @@ Frigate is a Docker container that can be run on any Docker host including as a
 
 :::tip
 
-If you already have Frigate installed as a Home Assistant App, check out the [getting started guide](../guides/getting_started.md#configuring-frigate) to configure Frigate.
+If you already have Frigate installed as a Home Assistant App, check out the [getting started guide](../guides/getting_started#configuring-frigate) to configure Frigate.
 
 :::
 
@@ -297,7 +297,7 @@ The MemryX MX3 Accelerator is available in the M.2 2280 form factor (like an NVM
 
 #### Installation
 
-To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/2p1/get_started/install_hardware.html).
+To get started with MX3 hardware setup for your system, refer to the [Hardware Setup Guide](https://developer.memryx.com/2p1/get_started/hardware_setup.html).
 
 Then follow these steps for installing the correct driver/runtime configuration:
 

notebooks/YOLO_NAS_Pretrained_Export.ipynb

@@ -1,95 +1,88 @@
 {
- "cells": [
+  "cells": [
-  {
+    {
-   "cell_type": "markdown",
+      "cell_type": "code",
-   "metadata": {
+      "execution_count": null,
-    "id": "runtime-notice"
+      "metadata": {
-   },
+        "id": "rmuF9iKWTbdk"
-   "source": [
+      },
-    "**Before running:** go to **Runtime → Change runtime type → Fallback runtime version: 2025.07** (Python 3.11). The current Colab default (Python 3.12+) is incompatible with `super-gradients`."
+      "outputs": [],
-   ]
+      "source": [
+        "! pip install -q git+https://github.com/Deci-AI/super-gradients.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NiRCt917KKcL"
+      },
+      "outputs": [],
+      "source": [
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.12/dist-packages/super_gradients/training/pretrained_models.py\n",
+        "! sed -i 's/sghub.deci.ai/sg-hub-nv.s3.amazonaws.com/' /usr/local/lib/python3.12/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dTB0jy_NNSFz"
+      },
+      "outputs": [],
+      "source": [
+        "from super_gradients.common.object_names import Models\n",
+        "from super_gradients.conversion import DetectionOutputFormatMode\n",
+        "from super_gradients.training import models\n",
+        "\n",
+        "model = models.get(Models.YOLO_NAS_S, pretrained_weights=\"coco\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GymUghyCNXem"
+      },
+      "outputs": [],
+      "source": [
+        "# export the model for compatibility with Frigate\n",
+        "\n",
+        "model.export(\"yolo_nas_s.onnx\",\n",
+        "             output_predictions_format=DetectionOutputFormatMode.FLAT_FORMAT,\n",
+        "             max_predictions_per_image=20,\n",
+        "             num_pre_nms_predictions=300,\n",
+        "             confidence_threshold=0.4,\n",
+        "             input_image_shape=(320,320),\n",
+        "            )"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uBhXV5g4Nh42"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import files\n",
+        "\n",
+        "files.download('yolo_nas_s.onnx')"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
   },
-  {
+  "nbformat": 4,
-   "cell_type": "code",
+  "nbformat_minor": 0
-   "execution_count": null,
+}
-   "metadata": {
-    "id": "rmuF9iKWTbdk"
-   },
-   "outputs": [],
-   "source": [
-    "! pip install -q \"jedi>=0.16\"\n",
-    "! pip install -q git+https://github.com/Deci-AI/super-gradients.git"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NiRCt917KKcL"
-   },
-   "outputs": [],
-   "source": "! sed -i 's/sghub\\.deci\\.ai/d2gjn4b69gu75n.cloudfront.net/g; s/sg-hub-nv\\.s3\\.amazonaws\\.com/d2gjn4b69gu75n.cloudfront.net/g' /usr/local/lib/python*/dist-packages/super_gradients/training/pretrained_models.py\n! sed -i 's/sghub\\.deci\\.ai/d2gjn4b69gu75n.cloudfront.net/g; s/sg-hub-nv\\.s3\\.amazonaws\\.com/d2gjn4b69gu75n.cloudfront.net/g' /usr/local/lib/python*/dist-packages/super_gradients/training/utils/checkpoint_utils.py"
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "dTB0jy_NNSFz"
-   },
-   "outputs": [],
-   "source": [
-    "from super_gradients.common.object_names import Models\n",
-    "from super_gradients.conversion import DetectionOutputFormatMode\n",
-    "from super_gradients.training import models\n",
-    "\n",
-    "model = models.get(Models.YOLO_NAS_S, pretrained_weights=\"coco\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "GymUghyCNXem"
-   },
-   "outputs": [],
-   "source": [
-    "# export the model for compatibility with Frigate\n",
-    "\n",
-    "model.export(\"yolo_nas_s.onnx\",\n",
-    "             output_predictions_format=DetectionOutputFormatMode.FLAT_FORMAT,\n",
-    "             max_predictions_per_image=20,\n",
-    "             num_pre_nms_predictions=300,\n",
-    "             confidence_threshold=0.4,\n",
-    "             input_image_shape=(320,320),\n",
-    "            )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "uBhXV5g4Nh42"
-   },
-   "outputs": [],
-   "source": [
-    "from google.colab import files\n",
-    "\n",
-    "files.download('yolo_nas_s.onnx')"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "name": "python3"
-  },
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}