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 `model.path`.
ui:Navigate to **Settings > System > Detectors and model** and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`.
yaml:|-
detectors:
coral:
type:edgetpu
device:usb
- key:yolov9
label:YOLOv9
recommended:false
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 `model.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."
ui:|-
Navigate to **Settings > System > Detectors and model** and select **EdgeTPU** from the detector type dropdown and click **Add**, then set device to `usb`. Then on the same page, in the **Custom Model** tab, 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.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **Hailo-8/Hailo-8L** from the detector type dropdown and click **Add**, then set device to `PCIe`. Then on the same page, in the **Custom Model** tab, configure the model settings:
# just make sure to give it the write configuration based on the model
- key:ssd
label:SSD MobileNet v1
recommended:false
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`.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **Hailo-8/Hailo-8L** from the detector type dropdown and click **Add**, then set device to `PCIe`. Then on the same page, in the **Custom Model** tab, 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 > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then on the same page, in the **Custom Model** tab, 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.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then on the same page, in the **Custom Model** tab, configure:
Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU` (or `NPU`). Then on the same page, in the **Custom Model** tab, 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.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU`. Then on the same page, in the **Custom Model** tab, configure:
width:320# <--- should match whatever was set in notebook
height:320# <--- should match whatever was set in notebook
input_tensor:nchw
input_pixel_format:bgr
path:/config/yolo_nas_s.onnx
labelmap_path:/labelmap/coco-80.txt
- key:yolox
label:YOLOX
recommended:false
download:YOLOx models can be downloaded [from the YOLOx repo](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/ONNXRuntime).
ui:|-
Navigate to **Settings > System > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU`. Then on the same page, in the **Custom Model** tab, 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 > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `GPU`. Then on the same page, in the **Custom Model** tab, 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 > Detectors and model** and select **OpenVINO** from the detector type dropdown and click **Add**, then set device to `CPU`. Then on the same page, in the **Custom Model** tab, 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 > Detectors and model** and select **ZMQ IPC** from the detector type dropdown and click **Add**, then set the endpoint to `tcp://host.docker.internal:5555`. Then on the same page, in the **Custom Model** tab, configure:
Navigate to **Settings > System > Detectors and model** and select **ZMQ IPC** from the detector type dropdown and click **Add**, then set the endpoint to `tcp://host.docker.internal:5555`. Then on the same page, in the **Custom Model** tab, 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 > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, 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 > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, 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.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
width:320# <--- should match whatever was set in notebook
height:320# <--- should match whatever was set in notebook
input_pixel_format:bgr
input_tensor:nchw
path:/config/yolo_nas_s.onnx
labelmap_path:/labelmap/coco-80.txt
- key:yolox
label:YOLOX
recommended:false
download:YOLOx models can be downloaded [from the YOLOx repo](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/ONNXRuntime).
ui:|-
Navigate to **Settings > System > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, 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 > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
Navigate to **Settings > System > Detectors and model** and select **ONNX** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, 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 `model.path`.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **CPU** from the detector type dropdown and click **Add**. Configure the number of threads and click **Add** again to add additional CPU detectors as needed (one per camera is recommended).
| Field | Value |
| ----------------- | ----- |
| **Detector type** | `cpu` |
| **Num threads** | `3` |
yaml:|-
detectors:
cpu1:
type:cpu
num_threads:3
deepstack:
title:DeepStack / CodeProject.AI
models:
- key:yolo
label:YOLO
recommended:true
download:This detector runs object detection over the network against a CodeProject.AI or DeepStack server, so no model is downloaded into Frigate itself. Visit the [CodeProject.AI official website](https://www.codeproject.com/Articles/5322557/CodeProject-AI-Server-AI-the-easy-way) to download and install the AI server on your preferred device (e.g. Raspberry Pi, Nvidia Jetson, or other compatible hardware) before configuring the detector.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **DeepStack** from the detector type dropdown and click **Add**. Set the API URL to point to your CodeProject.AI server (e.g., `http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection`).
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/`.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
width:320# (Can be set to 640 for higher resolution)
height:320# (Can be set to 640 for higher resolution)
input_tensor:nchw
input_dtype:float
labelmap_path:/labelmap/coco-80.txt
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/yolonas.zip
# The .zip file must contain:
# ├── yolonas.dfp (a file ending with .dfp)
# └── yolonas_post.onnx (optional; only if the model includes a cropped post-processing network)
- key:yolov9
label:YOLOv9
recommended:false
download:|-
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/`.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
width:320# (Can be set to 640 for higher resolution)
height:320# (Can be set to 640 for higher resolution)
input_tensor:nchw
input_dtype:float
labelmap_path:/labelmap/coco-80.txt
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/yolov9.zip
# The .zip file must contain:
# ├── yolov9.dfp (a file ending with .dfp)
- key:yolox
label:YOLOX
recommended:false
download:|-
The model is sourced from the [OpenCV Model Zoo](https://github.com/opencv/opencv_zoo) and precompiled to DFP.
MemryX `.dfp` models are automatically downloaded at runtime, if enabled, to the container at `/memryx_models/model_folder/`.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/yolox.zip
# The .zip file must contain:
# ├── yolox.dfp (a file ending with .dfp)
- key:ssd
label:SSDLite MobileNet v2
recommended:false
download:|-
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/`.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **MemryX** from the detector type dropdown and click **Add**, then set device to `PCIe:0`. Then on the same page, in the **Custom Model** tab, configure:
# Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
# path: /config/ssdlite_mobilenet.zip
# The .zip file must contain:
# ├── ssdlite_mobilenet.dfp (a file ending with .dfp)
# └── ssdlite_mobilenet_post.onnx (optional; only if the model includes a cropped post-processing network)
tensorrt:
title:TensorRT
models:
- key:yolo-legacy
label:YOLO (v3, v4, v7)
recommended:true
download:|-
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:
```yml
frigate:
environment:
- YOLO_MODELS=yolov7-320,yolov7x-640
- USE_FP16=false
```
ui:|-
Navigate to **Settings > System > Detectors and model** and select **TensorRT** from the detector type dropdown and click **Add**, then set the device to `0` (the default GPU index). Then on the same page, in the **Custom Model** tab, configure:
width:320# MUST match the chosen model i.e yolov7-320 -> 320, yolov4-416 -> 416
height:320# MUST match the chosen model i.e yolov7-320 -> 320 yolov4-416 -> 416
synaptics:
title:Synaptics
models:
- key:ssd
label:SSD MobileNet
recommended:true
download:A synap model is provided in the container at `/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).
ui:|-
Navigate to **Settings > System > Detectors and model** and select **Synaptics** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
input_tensor:nhwc# default value (optional. If you change the model, it is required)
labelmap_path:/labelmap/coco-80.txt# required
rknn:
title:RKNN
models:
- key:yolov9
label:YOLOv9
recommended:true
download:|-
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.
ui:|-
Navigate to **Settings > System > Detectors and model** and, in the **Custom Model** tab, configure:
# name of model (will be automatically downloaded) or path to your own .rknn model file
# possible values are:
# - frigate-fp16-yolov9-t
# - frigate-fp16-yolov9-s
# - frigate-fp16-yolov9-m
# - frigate-fp16-yolov9-c
# - frigate-fp16-yolov9-e
# your yolo_model.rknn
path:frigate-fp16-yolov9-t
model_type:yolo-generic
width:320
height:320
input_tensor:nhwc
labelmap_path:/labelmap/coco-80.txt
- key:yolonas
label:YOLO-NAS
recommended:false
download:|-
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
ui:|-
Navigate to **Settings > System > Detectors and model** and, in the **Custom Model** tab, configure:
# name of model (will be automatically downloaded) or path to your own .rknn model file
# possible values are:
# - deci-fp16-yolonas_s
# - deci-fp16-yolonas_m
# - deci-fp16-yolonas_l
# your yolonas_model.rknn
path:deci-fp16-yolonas_s
model_type:yolonas
width:320
height:320
input_pixel_format:bgr
input_tensor:nhwc
labelmap_path:/labelmap/coco-80.txt
- key:yolox
label:YOLOx
recommended:false
download:|-
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.
ui:|-
Navigate to **Settings > System > Detectors and model** and, in the **Custom Model** tab, configure:
# name of model (will be automatically downloaded) or path to your own .rknn model file
# possible values are:
# - rock-i8-yolox_nano
# - rock-i8-yolox_tiny
# - rock-fp16-yolox_nano
# - rock-fp16-yolox_tiny
# your yolox_model.rknn
path:rock-i8-yolox_nano
model_type:yolox
width:416
height:416
input_tensor:nhwc
labelmap_path:/labelmap/coco-80.txt
axengine:
title:AXEngine
models:
- key:yolov9
label:YOLOv9
recommended:true
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.
ui:|-
Navigate to **Settings > System > Detectors and model** and select **AXEngine NPU** from the detector type dropdown and click **Add**. Then on the same page, in the **Custom Model** tab, configure:
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) |
