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Refactor detector and model management (#23995)
* Refactor detector and model management * Fix model resolution field
This commit is contained in:
Nicolas Mowen
committed by
Josh Hawkins
Josh Hawkins
parent
7b42d94bfe
commit
5c9c02002f
@@ -98,6 +98,10 @@
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"label": "Detect width",
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"description": "Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution."
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},
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"scene": {
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"label": "Detect scene",
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"description": "The environment this camera looks at, used to pick which of the configured models runs on it. Defaults to the model with a scene of 'all'."
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},
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"fps": {
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"label": "Detect FPS",
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"description": "Desired frames per second to run detection on; lower values reduce CPU usage (recommended value is 5, only set higher - at most 10 - if tracking extremely fast moving objects)."
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@@ -275,172 +275,17 @@
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"description": "Unit system for display (metric or imperial) used in the UI and MQTT."
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}
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},
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"detectors": {
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"label": "Detector hardware",
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"description": "Configuration for object detectors (CPU, GPU, ONNX backends) and any detector-specific model settings.",
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"type": {
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"label": "Type"
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"models": {
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"label": "Detection models",
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"description": "Object detection models and the hardware each one runs on. Cameras pick a model by matching their detect.scene against a model's scene.",
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"scene": {
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"label": "Model scene",
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"description": "The camera environment this model is used for. Cameras select a model by setting detect.scene to a matching value, and 'all' is used by any camera that does not set one."
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},
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"model": {
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"label": "Detector specific model configuration",
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"description": "Detector-specific model configuration options (path, input size, etc.).",
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"path": {
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"label": "Custom object detector model path",
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"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
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},
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"labelmap_path": {
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"label": "Label map for custom object detector",
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"description": "Path to a labelmap file that maps numeric classes to string labels for the detector."
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},
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"width": {
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"label": "Object detection model input width",
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"description": "Width of the model input tensor in pixels."
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},
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"height": {
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"label": "Object detection model input height",
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"description": "Height of the model input tensor in pixels."
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},
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"labelmap": {
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"label": "Labelmap customization",
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"description": "Overrides or remapping entries to merge into the standard labelmap."
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},
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"attributes_map": {
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"label": "Map of object labels to their attribute labels",
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"description": "Mapping from object labels to attribute labels used to attach metadata (for example 'car' -> ['license_plate'])."
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},
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"input_tensor": {
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"label": "Model Input Tensor Shape",
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"description": "Tensor format expected by the model: 'nhwc' or 'nchw'."
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},
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"input_pixel_format": {
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"label": "Model Input Pixel Color Format",
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"description": "Pixel colorspace expected by the model: 'rgb', 'bgr', or 'yuv'."
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},
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"input_dtype": {
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"label": "Model Input D Type",
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"description": "Data type of the model input tensor (for example 'float32')."
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},
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"model_type": {
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"label": "Object Detection Model Type",
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"description": "Detector model architecture type (ssd, yolox, yolonas, yolo-generic, rfdetr, dfine) used by some detectors for optimization."
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}
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"devices": {
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"label": "Detection hardware",
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"description": "Hardware this model runs on, as '<detector>' or '<detector>:<device>' (for example 'edgetpu:pci:0' or 'openvino:GPU'). Listing the same device more than once runs additional inference processes on it."
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},
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"model_path": {
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"label": "Detector specific model path",
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"description": "File path to the detector model binary if required by the chosen detector."
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},
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"axengine": {
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"label": "AXEngine NPU",
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"description": "AXERA AX650N/AX8850N NPU detector running compiled .axmodel files via the AXEngine runtime."
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},
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"cpu": {
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"label": "CPU",
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"description": "CPU TFLite detector that runs TensorFlow Lite models on the host CPU without hardware acceleration. Not recommended.",
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"num_threads": {
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"label": "Number of detection threads",
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"description": "The number of threads used for CPU-based inference."
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}
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},
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"deepstack": {
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"label": "DeepStack",
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"description": "DeepStack/CodeProject.AI detector that sends images to a remote DeepStack HTTP API for inference. Not recommended.",
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"api_url": {
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"label": "DeepStack API URL",
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"description": "The URL of the DeepStack API."
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},
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"api_timeout": {
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"label": "DeepStack API timeout (in seconds)",
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"description": "Maximum time allowed for a DeepStack API request."
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},
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"api_key": {
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"label": "DeepStack API key (if required)",
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"description": "Optional API key for authenticated DeepStack services."
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}
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},
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"edgetpu": {
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"label": "EdgeTPU",
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"description": "EdgeTPU detector that runs TensorFlow Lite models compiled for Coral EdgeTPU using the EdgeTPU delegate.",
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"device": {
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"label": "Device Type",
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"description": "The device to use for EdgeTPU inference (e.g. 'usb', 'pci')."
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}
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},
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"hailo8l": {
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"label": "Hailo-8/Hailo-8L",
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"description": "Hailo-8/Hailo-8L detector using HEF models and the HailoRT SDK for inference on Hailo hardware.",
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"device": {
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"label": "Device Type",
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"description": "The device to use for Hailo inference (e.g. 'PCIe', 'M.2')."
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}
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},
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"memryx": {
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"label": "MemryX",
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"description": "MemryX MX3 detector that runs compiled DFP models on MemryX accelerators.",
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"device": {
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"label": "Device Path",
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"description": "The device to use for MemryX inference (e.g. 'PCIe')."
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}
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},
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"onnx": {
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"label": "ONNX",
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"description": "ONNX detector for running ONNX models; will use available acceleration backends (CUDA/ROCm/OpenVINO) when available.",
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"device": {
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"label": "Device Type",
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"description": "The device to use for ONNX inference (e.g. 'AUTO', 'CPU', 'GPU')."
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}
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},
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"openvino": {
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"label": "OpenVINO",
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"description": "OpenVINO detector for AMD and Intel CPUs, Intel GPUs and Intel VPU hardware.",
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"device": {
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"label": "Device Type",
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"description": "The device to use for OpenVINO inference (e.g. 'CPU', 'GPU', 'NPU')."
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}
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},
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"rknn": {
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"label": "RKNN",
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"description": "RKNN detector for Rockchip NPUs; runs compiled RKNN models on Rockchip hardware.",
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"num_cores": {
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"label": "Number of NPU cores to use.",
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"description": "The number of NPU cores to use (0 for auto)."
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}
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},
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"synaptics": {
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"label": "Synaptics",
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"description": "Synaptics NPU detector for models in .synap format using the Synap SDK on Synaptics hardware."
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},
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"teflon_tfl": {
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"label": "Teflon",
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"description": "Teflon delegate detector for TFLite using Mesa Teflon delegate library to accelerate inference on supported GPUs."
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},
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"tensorrt": {
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"label": "TensorRT",
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"description": "TensorRT detector for Nvidia Jetson devices using serialized TensorRT engines for accelerated inference.",
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"device": {
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"label": "GPU Device Index",
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"description": "The GPU device index to use."
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}
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},
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"zmq": {
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"label": "ZMQ IPC",
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"description": "ZMQ IPC detector that offloads inference to an external process via a ZeroMQ IPC endpoint.",
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"endpoint": {
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"label": "ZMQ IPC endpoint",
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"description": "The ZMQ endpoint to connect to."
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},
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"request_timeout_ms": {
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"label": "ZMQ request timeout in milliseconds",
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"description": "Timeout for ZMQ requests in milliseconds."
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},
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"linger_ms": {
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"label": "ZMQ socket linger in milliseconds",
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"description": "Socket linger period in milliseconds."
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}
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}
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},
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"model": {
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"label": "Detection model",
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"description": "Settings to configure a custom object detection model and its input shape.",
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"path": {
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"label": "Custom object detector model path",
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"description": "Path to a custom detection model file (or plus://<model_id> for Frigate+ models)."
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@@ -621,6 +466,10 @@
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"label": "Detect width",
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"description": "Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution."
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},
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"scene": {
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"label": "Detect scene",
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"description": "The environment this camera looks at, used to pick which of the configured models runs on it. Defaults to the model with a scene of 'all'."
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},
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"fps": {
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"label": "Detect FPS",
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"description": "Desired frames per second to run detection on; lower values reduce CPU usage (recommended value is 5, only set higher - at most 10 - if tracking extremely fast moving objects)."
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