diff --git a/docker/tensorrt/requirements-amd64.txt b/docker/tensorrt/requirements-amd64.txt index a7853aeecf..63c68b5832 100644 --- a/docker/tensorrt/requirements-amd64.txt +++ b/docker/tensorrt/requirements-amd64.txt @@ -13,7 +13,6 @@ nvidia_cusolver_cu12==11.6.3.*; platform_machine == 'x86_64' nvidia_cusparse_cu12==12.5.1.*; platform_machine == 'x86_64' nvidia_nccl_cu12==2.23.4; platform_machine == 'x86_64' nvidia_nvjitlink_cu12==12.5.82; platform_machine == 'x86_64' -tensorflow==2.19.*; platform_machine == 'x86_64' onnx==1.16.*; platform_machine == 'x86_64' onnxruntime-gpu==1.22.*; platform_machine == 'x86_64' protobuf==3.20.3; platform_machine == 'x86_64' diff --git a/docs/docs/configuration/custom_classification/object_classification.md b/docs/docs/configuration/custom_classification/object_classification.md index 983fce8526..cff8a6cad4 100644 --- a/docs/docs/configuration/custom_classification/object_classification.md +++ b/docs/docs/configuration/custom_classification/object_classification.md @@ -10,7 +10,6 @@ Object classification allows you to train a custom MobileNetV2 classification mo Object classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate. Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer. -When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training. ## Classes diff --git a/docs/docs/configuration/custom_classification/state_classification.md b/docs/docs/configuration/custom_classification/state_classification.md index c22661f267..caaeaed5a5 100644 --- a/docs/docs/configuration/custom_classification/state_classification.md +++ b/docs/docs/configuration/custom_classification/state_classification.md @@ -10,7 +10,6 @@ State classification allows you to train a custom MobileNetV2 classification mod State classification models are lightweight and run very fast on CPU. Inference should be usable on virtually any machine that can run Frigate. Training the model does briefly use a high amount of system resources for about 1–3 minutes per training run. On lower-power devices, training may take longer. -When running the `-tensorrt` image, Nvidia GPUs will automatically be used to accelerate training. ## Classes diff --git a/frigate/genai/__init__.py b/frigate/genai/__init__.py index 90b8f64cf5..4d789d77ed 100644 --- a/frigate/genai/__init__.py +++ b/frigate/genai/__init__.py @@ -114,7 +114,7 @@ Your response MUST be a flat JSON object with: ## Objects in Scene -Each line represents a detection state, not necessarily unique individuals. Objects with names in parentheses (e.g., "Name (person)") are verified identities. Objects without names (e.g., "Person") are detected but not identified. +Each line represents a detection state, not necessarily unique individuals. Parentheses indicate object type or category, use only the name/label in your response, not the parentheses. **CRITICAL: When you see both recognized and unrecognized entries of the same type (e.g., "Joe (person)" and "Person"), visually count how many distinct people/objects you actually see based on appearance and clothing. If you observe only ONE person throughout the sequence, use ONLY the recognized name (e.g., "Joe"). The same person may be recognized in some frames but not others. Only describe both if you visually see MULTIPLE distinct people with clearly different appearances.** diff --git a/frigate/genai/openai.py b/frigate/genai/openai.py index 046a18aa9a..2a21b8c2e7 100644 --- a/frigate/genai/openai.py +++ b/frigate/genai/openai.py @@ -18,6 +18,7 @@ class OpenAIClient(GenAIClient): """Generative AI client for Frigate using OpenAI.""" provider: OpenAI + context_size: Optional[int] = None def _init_provider(self): """Initialize the client.""" @@ -69,5 +70,33 @@ class OpenAIClient(GenAIClient): def get_context_size(self) -> int: """Get the context window size for OpenAI.""" - # OpenAI GPT-4 Vision models have 128K token context window - return 128000 + if self.context_size is not None: + return self.context_size + + try: + models = self.provider.models.list() + for model in models.data: + if model.id == self.genai_config.model: + if hasattr(model, "max_model_len") and model.max_model_len: + self.context_size = model.max_model_len + logger.debug( + f"Retrieved context size {self.context_size} for model {self.genai_config.model}" + ) + return self.context_size + + except Exception as e: + logger.debug( + f"Failed to fetch model context size from API: {e}, using default" + ) + + # Default to 128K for ChatGPT models, 8K for others + model_name = self.genai_config.model.lower() + if "gpt-4o" in model_name: + self.context_size = 128000 + else: + self.context_size = 8192 + + logger.debug( + f"Using default context size {self.context_size} for model {self.genai_config.model}" + ) + return self.context_size diff --git a/frigate/util/config.py b/frigate/util/config.py index 56f5662fc2..5a14b1fa6f 100644 --- a/frigate/util/config.py +++ b/frigate/util/config.py @@ -384,10 +384,10 @@ def migrate_017_0(config: dict[str, dict[str, Any]]) -> dict[str, dict[str, Any] new_object_config["genai"] = {} for key in global_genai.keys(): - if key not in ["enabled", "model", "provider", "base_url", "api_key"]: - new_object_config["genai"][key] = global_genai[key] - else: + if key in ["model", "provider", "base_url", "api_key"]: new_genai_config[key] = global_genai[key] + else: + new_object_config["genai"][key] = global_genai[key] config["genai"] = new_genai_config diff --git a/web/public/locales/en/views/classificationModel.json b/web/public/locales/en/views/classificationModel.json index 4b268480f6..fc49c2ff42 100644 --- a/web/public/locales/en/views/classificationModel.json +++ b/web/public/locales/en/views/classificationModel.json @@ -86,6 +86,7 @@ "classificationSubLabel": "Sub Label", "classificationAttribute": "Attribute", "classes": "Classes", + "states": "States", "classesTip": "Learn about classes", "classesStateDesc": "Define the different states your camera area can be in. For example: 'open' and 'closed' for a garage door.", "classesObjectDesc": "Define the different categories to classify detected objects into. For example: 'delivery_person', 'resident', 'stranger' for person classification.", diff --git a/web/src/components/classification/wizard/Step1NameAndDefine.tsx b/web/src/components/classification/wizard/Step1NameAndDefine.tsx index b158782dcc..8a510d33db 100644 --- a/web/src/components/classification/wizard/Step1NameAndDefine.tsx +++ b/web/src/components/classification/wizard/Step1NameAndDefine.tsx @@ -394,7 +394,9 @@ export default function Step1NameAndDefine({
- {t("wizard.step1.classes")} + {watchedModelType === "state" + ? t("wizard.step1.states") + : t("wizard.step1.classes")}