download:A TensorFlow Lite model is provided in the container at `/edgetpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with a model's `path`.
ui:Navigate to **Settings > System > Detection models** and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown.
download:"[Download the model](https://github.com/dbro/frigate-detector-edgetpu-yolo9/releases/download/v1.0/yolov9-s-relu6-best_320_int8_edgetpu.tflite), bind mount the file into the container, and provide the path with a model's `path`. Note that the linked model requires a 17-label [labelmap file](https://raw.githubusercontent.com/dbro/frigate-detector-edgetpu-yolo9/refs/heads/main/labels-coco17.txt) that includes only 17 COCO classes."
Navigate to **Settings > System > Detection models** and select **Coral EdgeTPU (USB)** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure the model settings:
download:If no custom model path or URL is provided, the Hailo detector automatically downloads the default model (YOLOv6n) from the Hailo Model Zoo on first startup based on the detected hardware. Once cached under `/config/model_cache/hailo`, the model works fully offline.
Navigate to **Settings > System > Detection models** and select **Hailo** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure the model settings:
download:For SSD-based models, provide either a model path or URL to your compiled SSD model. The integration will first check the local path before downloading if necessary. The model file is cached under `/config/model_cache/hailo`.
Navigate to **Settings > System > Detection models** and select **Hailo** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure the model settings:
YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).
Navigate to **Settings > System > Detection models** and select **Intel GPU** (or **Intel NPU**) from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
download:An OpenVINO model is provided in the container at `/openvino-model/ssdlite_mobilenet_v2.xml` and is used by this detector type by default. The model comes from Intel's Open Model Zoo [SSDLite MobileNet V2](https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssdlite_mobilenet_v2) and is converted to an FP16 precision IR model.
Navigate to **Settings > System > Detection models** and select **Intel GPU** (or **Intel NPU**) from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
Navigate to **Settings > System > Detection models** and select **Intel GPU** (or **Intel NPU**) from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) which can be run directly in [Google Colab](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
:::warning
The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
:::
The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
Navigate to **Settings > System > Detection models** and select **Intel GPU** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
Navigate to **Settings > System > Detection models** and select **Intel GPU** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
RF-DETR can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=Nano` in the first line to `Nano`, `Small`, or `Medium` size.
Navigate to **Settings > System > Detection models** and select **Intel GPU** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
D-FINE can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=s` in the first line to `s`, `m`, or `l` size.
[DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) can be exported as ONNX by running the command below. Pretrained weights are available on Hugging Face for two backbone families:
Set `BACKBONE` and `MODEL_SIZE` in the first line to match your desired variant. Hugging Face model names use uppercase (e.g. `HGNetv2_N`, `DINOv3_S`), while config files use lowercase (e.g. `hgnetv2_n`, `dinov3_s`).
Navigate to **Settings > System > Detection models** and select **CPU** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).
Navigate to **Settings > System > Detection models** and add a model. The ZMQ endpoint is not reported by the hardware probe, so set `devices` to `zmq:tcp://host.docker.internal:5555` in YAML. Then, on the same model, open the **Custom Model** tab and configure:
Navigate to **Settings > System > Detection models** and add a model. The ZMQ endpoint is not reported by the hardware probe, so set `devices` to `zmq:tcp://host.docker.internal:5555` in YAML. Then, on the same model, open the **Custom Model** tab and configure:
YOLOv9 model can be exported as ONNX using the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=t` and `IMG_SIZE=320` in the first line to the [model size](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available model sizes are `t`, `s`, `m`, `c`, and `e`, common image sizes are `320` and `640`).
Navigate to **Settings > System > Detection models** and select your GPU from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
RF-DETR can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=Nano` in the first line to `Nano`, `Small`, or `Medium` size.
Navigate to **Settings > System > Detection models** and select your GPU from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
You can build and download a compatible model with pre-trained weights using [this notebook](https://github.com/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) [](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb) which can be run directly in [Google Colab](https://colab.research.google.com/github/blakeblackshear/frigate/blob/dev/notebooks/YOLO_NAS_Pretrained_Export.ipynb).
:::warning
The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
:::
The input image size in this notebook is set to 320x320. This results in lower CPU usage and faster inference times without impacting performance in most cases due to the way Frigate crops video frames to areas of interest before running detection. The notebook and config can be updated to 640x640 if desired.
Navigate to **Settings > System > Detection models** and select your GPU from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
Navigate to **Settings > System > Detection models** and select your GPU from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
D-FINE can be exported as ONNX by running the command below. You can copy and paste the whole thing to your terminal and execute, altering `MODEL_SIZE=s` in the first line to `s`, `m`, or `l` size.
[DEIMv2](https://github.com/Intellindust-AI-Lab/DEIMv2) can be exported as ONNX by running the command below. Pretrained weights are available on Hugging Face for two backbone families:
Set `BACKBONE` and `MODEL_SIZE` in the first line to match your desired variant. Hugging Face model names use uppercase (e.g. `HGNetv2_N`, `DINOv3_S`), while config files use lowercase (e.g. `hgnetv2_n`, `dinov3_s`).
Navigate to **Settings > System > Detection models** and select your GPU from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
Navigate to **Settings > System > Detection models** and select your GPU from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
download:A TensorFlow Lite model is provided in the container at `/cpu_model.tflite` and is used by this detector type by default. To provide your own model, bind mount the file into the container and provide the path with a model's `path`.
Navigate to **Settings > System > Detection models** and select **CPU** from the **Hardware** dropdown and set **Detectors** to the number of detection processes to run (one per camera is recommended).
The [YOLO-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md) model included in this detector is downloaded automatically and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
**Note:** The default model for the MemryX detector is YOLO-NAS 320x320.
The input size for **YOLO-NAS** can be set to either **320x320** (default) or **640x640**.
- The default size of **320x320** is optimized for lower CPU usage and faster inference times.
MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the container at `/memryx_models/model_folder/`.
Navigate to **Settings > System > Detection models** and select **MemryX MX3** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
The YOLOv9s model included in this detector is downloaded from [the original GitHub](https://github.com/WongKinYiu/yolov9) and compiled to DFP with [mx_nc](https://developer.memryx.com/2p1/tools/neural_compiler.html#usage).
MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the container at `/memryx_models/model_folder/`.
Navigate to **Settings > System > Detection models** and select **MemryX MX3** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
Navigate to **Settings > System > Detection models** and select **MemryX MX3** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
The model is sourced from the [OpenMMLab Model Zoo](https://mmdeploy-oss.openmmlab.com/model/mmdet-det/ssdlite-e8679f.onnx) and has been converted to DFP.
MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the container at `/memryx_models/model_folder/`.
Navigate to **Settings > System > Detection models** and select **MemryX MX3** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
The model used for TensorRT must be preprocessed on the same hardware platform that it will run on, so Frigate generates the `.trt` model file on-device at startup. Processed models are stored in the `/config/model_cache` folder.
By default no models are generated. Set the `YOLO_MODELS` environment variable in Docker to one or more comma-separated model names (from the available `yolov3`/`yolov4`/`yolov7` models) and each one will be generated on startup if the corresponding `{model}.trt` file is not already present in `model_cache` (delete it to force regeneration). On Jetson devices with DLAs (Xavier or Orin), append `-dla` to a model name to generate a DLA model. If your GPU does not support FP16 operations, pass `USE_FP16=False` to disable it.
An example `docker-compose.yml` fragment that converts the `yolov7-320` and `yolov7x-640` models:
Navigate to **Settings > System > Detection models** and select **NVIDIA Jetson** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
download:A synap model is provided in the container at `/synaptics/mobilenet.synap` and is used by this detector type by default. The model comes from the [Synap-release Github](https://github.com/synaptics-astra/synap-release/tree/v1.5.0/models/dolphin/object_detection/coco/model/mobilenet224_full80).
Navigate to **Settings > System > Detection models** and select **Synaptics NPU** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
If no custom model is provided, the RKNN detector downloads a default model from GitHub on first startup. Once cached, the model works fully offline. All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
Navigate to **Settings > System > Detection models** select **Rockchip NPU** from the **Hardware** dropdown, then open the **Custom Model** tab and configure:
If no custom model is provided, the RKNN detector downloads a default model from GitHub on first startup. Once cached, the model works fully offline. All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
**Note:** The pre-trained YOLO-NAS weights from DeciAI are subject to their license and can't be used commercially. For more information, see: https://docs.deci.ai/super-gradients/latest/LICENSE.YOLONAS.html
Navigate to **Settings > System > Detection models** select **Rockchip NPU** from the **Hardware** dropdown, then open the **Custom Model** tab and configure:
If no custom model is provided, the RKNN detector downloads a default model from GitHub on first startup. Once cached, the model works fully offline. All models are automatically downloaded and stored in the folder `config/model_cache/rknn_cache`. After upgrading Frigate, you should remove older models to free up space.
You can also provide your own `.rknn` model. You should not save your own models in the `rknn_cache` folder, store them directly in the `model_cache` folder or another subfolder. To convert a model to `.rknn` format see the `rknn-toolkit2` (requires a x86 machine). Note, that there is only post-processing for the supported models.
Navigate to **Settings > System > Detection models** select **Rockchip NPU** from the **Hardware** dropdown, then open the **Custom Model** tab and configure:
download:A yolov9 axmodel is provided in the container at `/axmodels` and is used by this detector type by default. The AXEngine detector downloads its default model from HuggingFace on first startup; once cached, the model works fully offline.
Navigate to **Settings > System > Detection models** and select **AXERA NPU** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
