Supported Hardware

info

Frigate supports multiple different detectors that work on different types of hardware:

Most Hardware

Coral EdgeTPU: The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
Hailo: The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
MemryX: The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
DeGirum: Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on their website.

AMD

ROCm: ROCm can run on AMD Discrete GPUs to provide efficient object detection.
ONNX: ROCm will automatically be detected and used as a detector in the -rocm Frigate image when a supported ONNX model is configured.

Apple Silicon

Apple Silicon: Apple Silicon can run on M1 and newer Apple Silicon devices.

Intel

OpenVino: OpenVino can run on Intel Arc GPUs, Intel integrated GPUs, and Intel CPUs to provide efficient object detection.
ONNX: OpenVINO will automatically be detected and used as a detector in the default Frigate image when a supported ONNX model is configured.

Nvidia GPU

ONNX: TensorRT will automatically be detected and used as a detector in the -tensorrt Frigate image when a supported ONNX model is configured.

Nvidia Jetson

TensortRT: TensorRT can run on Jetson devices, using one of many default models.
ONNX: TensorRT will automatically be detected and used as a detector in the -tensorrt-jp6 Frigate image when a supported ONNX model is configured.

Rockchip

RKNN: RKNN models can run on Rockchip devices with included NPUs.

For Testing

CPU Detector (not recommended for actual use: Use a CPU to run tflite model, this is not recommended and in most cases OpenVINO can be used in CPU mode with better results.

note

Multiple detectors can not be mixed for object detection (ex: OpenVINO and Coral EdgeTPU can not be used for object detection at the same time).

This does not affect using hardware for accelerating other tasks such as semantic search

Officially Supported Detectors

Frigate provides the following builtin detector types: cpu, edgetpu, hailo8l, memryx, onnx, openvino, rknn, and tensorrt. By default, Frigate will use a single CPU detector. Other detectors may require additional configuration as described below. When using multiple detectors they will run in dedicated processes, but pull from a common queue of detection requests from across all cameras.

Edge TPU Detector

The Edge TPU detector type runs a TensorFlow Lite model utilizing the Google Coral delegate for hardware acceleration. To configure an Edge TPU detector, set the "type" attribute to "edgetpu".

The Edge TPU device can be specified using the "device" attribute according to the Documentation for the TensorFlow Lite Python API. If not set, the delegate will use the first device it finds.

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.

tip

See common Edge TPU troubleshooting steps if the Edge TPU is not detected.

Single USB Coral

detectors:
  coral:
    type: edgetpu
    device: usb

Multiple USB Corals

detectors:
  coral1:
    type: edgetpu
    device: usb:0
  coral2:
    type: edgetpu
    device: usb:1

Native Coral (Dev Board)

warning: may have compatibility issues after v0.9.x

detectors:
  coral:
    type: edgetpu
    device: ""

Single PCIE/M.2 Coral

detectors:
  coral:
    type: edgetpu
    device: pci

Multiple PCIE/M.2 Corals

detectors:
  coral1:
    type: edgetpu
    device: pci:0
  coral2:
    type: edgetpu
    device: pci:1

Mixing Corals

detectors:
  coral_usb:
    type: edgetpu
    device: usb
  coral_pci:
    type: edgetpu
    device: pci

Hailo-8

This detector is available for use with both Hailo-8 and Hailo-8L AI Acceleration Modules. The integration automatically detects your hardware architecture via the Hailo CLI and selects the appropriate default model if no custom model is specified.

See the installation docs for information on configuring the Hailo hardware.

Configuration

When configuring the Hailo detector, you have two options to specify the model: a local path or a URL. If both are provided, the detector will first check for the model at the given local path. If the file is not found, it will download the model from the specified URL. The model file is cached under /config/model_cache/hailo.

YOLO

Use this configuration for YOLO-based models. When no custom model path or URL is provided, the detector automatically downloads the default model based on the detected hardware:

Hailo-8 hardware: Uses YOLOv6n (default: yolov6n.hef)
Hailo-8L hardware: Uses YOLOv6n (default: yolov6n.hef)

detectors:
  hailo:
    type: hailo8l
    device: PCIe

model:
  width: 320
  height: 320
  input_tensor: nhwc
  input_pixel_format: rgb
  input_dtype: int
  model_type: yolo-generic
  labelmap_path: /labelmap/coco-80.txt

  # The detector automatically selects the default model based on your hardware:
  # - For Hailo-8 hardware: YOLOv6n (default: yolov6n.hef)
  # - For Hailo-8L hardware: YOLOv6n (default: yolov6n.hef)
  #
  # Optionally, you can specify a local model path to override the default.
  # If a local path is provided and the file exists, it will be used instead of downloading.
  # Example:
  # path: /config/model_cache/hailo/yolov6n.hef
  #
  # You can also override using a custom URL:
  # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8/yolov6n.hef
  # just make sure to give it the write configuration based on the model

SSD

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.

detectors:
  hailo:
    type: hailo8l
    device: PCIe

model:
  width: 300
  height: 300
  input_tensor: nhwc
  input_pixel_format: rgb
  model_type: ssd
  # Specify the local model path (if available) or URL for SSD MobileNet v1.
  # Example with a local path:
  # path: /config/model_cache/h8l_cache/ssd_mobilenet_v1.hef
  #
  # Or override using a custom URL:
  # path: https://hailo-model-zoo.s3.eu-west-2.amazonaws.com/ModelZoo/Compiled/v2.14.0/hailo8l/ssd_mobilenet_v1.hef

Custom Models

The Hailo detector supports all YOLO models compiled for Hailo hardware that include post-processing. You can specify a custom URL or a local path to download or use your model directly. If both are provided, the detector checks the local path first.

detectors:
  hailo:
    type: hailo8l
    device: PCIe

model:
  width: 640
  height: 640
  input_tensor: nhwc
  input_pixel_format: rgb
  input_dtype: int
  model_type: yolo-generic
  labelmap_path: /labelmap/coco-80.txt
  # Optional: Specify a local model path.
  # path: /config/model_cache/hailo/custom_model.hef
  #
  # Alternatively, or as a fallback, provide a custom URL:
  # path: https://custom-model-url.com/path/to/model.hef

For additional ready-to-use models, please visit: https://github.com/hailo-ai/hailo_model_zoo

Hailo8 supports all models in the Hailo Model Zoo that include HailoRT post-processing. You're welcome to choose any of these pre-configured models for your implementation.

Note: The config.path parameter can accept either a local file path or a URL ending with .hef. When provided, the detector will first check if the path is a local file path. If the file exists locally, it will use it directly. If the file is not found locally or if a URL was provided, it will attempt to download the model from the specified URL.

OpenVINO Detector

The OpenVINO detector type runs an OpenVINO IR model on AMD and Intel CPUs, Intel GPUs and Intel VPU hardware. To configure an OpenVINO detector, set the "type" attribute to "openvino".

The OpenVINO device to be used is specified using the "device" attribute according to the naming conventions in the Device Documentation. The most common devices are CPU and GPU. Currently, there is a known issue with using AUTO. For backwards compatibility, Frigate will attempt to use GPU if AUTO is set in your configuration.

OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. It will also run on AMD CPUs despite having no official support for it. A supported Intel platform is required to use the GPU device with OpenVINO. For detailed system requirements, see OpenVINO System Requirements

tip

When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:

detectors:
  ov_0:
    type: openvino
    device: GPU
  ov_1:
    type: openvino
    device: GPU

OpenVINO Supported Models

SSDLite MobileNet v2

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 and is converted to an FP16 precision IR model.

Use the model configuration shown below when using the OpenVINO detector with the default OpenVINO model:

detectors:
  ov:
    type: openvino
    device: GPU

model:
  width: 300
  height: 300
  input_tensor: nhwc
  input_pixel_format: bgr
  path: /openvino-model/ssdlite_mobilenet_v2.xml
  labelmap_path: /openvino-model/coco_91cl_bkgr.txt

YOLOX

This detector also supports YOLOX. Frigate does not come with any YOLOX models preloaded, so you will need to supply your own models.

YOLO-NAS

YOLO-NAS models are supported, but not included by default. See the models section for more information on downloading the YOLO-NAS model for use in Frigate.

After placing the downloaded onnx model in your config folder, you can use the following configuration:

detectors:
  ov:
    type: openvino
    device: GPU

model:
  model_type: yolonas
  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

Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.

YOLO (v3, v4, v7, v9)

YOLOv3, YOLOv4, YOLOv7, and YOLOv9 models are supported, but not included by default.

tip

The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv9 models, but may support other YOLO model architectures as well.

After placing the downloaded onnx model in your config folder, you can use the following configuration:

detectors:
  ov:
    type: openvino
    device: GPU

model:
  model_type: yolo-generic
  width: 320 # <--- should match the imgsize set during model export
  height: 320 # <--- should match the imgsize set during model export
  input_tensor: nchw
  input_dtype: float
  path: /config/model_cache/yolo.onnx
  labelmap_path: /labelmap/coco-80.txt

Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.

RF-DETR

RF-DETR is a DETR based model. The ONNX exported models are supported, but not included by default. See the models section for more informatoin on downloading the RF-DETR model for use in Frigate.

warning

Due to the size and complexity of the RF-DETR model, it is only recommended to be run with discrete Arc Graphics Cards.

After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:

detectors:
  ov:
    type: openvino
    device: GPU

model:
  model_type: rfdetr
  width: 320
  height: 320
  input_tensor: nchw
  input_dtype: float
  path: /config/model_cache/rfdetr.onnx

D-FINE

D-FINE is a DETR based model. The ONNX exported models are supported, but not included by default. See the models section for more information on downloading the D-FINE model for use in Frigate.

warning

Currently D-FINE models only run on OpenVINO in CPU mode, GPUs currently fail to compile the model

After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:

detectors:
  ov:
    type: openvino
    device: GPU

model:
  model_type: dfine
  width: 640
  height: 640
  input_tensor: nchw
  input_dtype: float
  path: /config/model_cache/dfine_s_obj2coco.onnx
  labelmap_path: /labelmap/coco-80.txt

Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.

Apple Silicon detector

The NPU in Apple Silicon can't be accessed from within a container, so the Apple Silicon detector client must first be setup. It is recommended to use the Frigate docker image with -standard-arm64 suffix, for example ghcr.io/blakeblackshear/frigate:stable-arm64-standard.

Setup

Setup the Apple Silicon detector client and run the client
Configure the detector in Frigate and startup Frigate

Configuration

Using the detector config below will connect to the client:

detectors:
  apple-silicon:
    type: zmq
    endpoint: tcp://host.docker.internal:5555

Apple Silicon Supported Models

There is no default model provided, the following formats are supported:

YOLO (v3, v4, v7, v9)

YOLOv3, YOLOv4, YOLOv7, and YOLOv9 models are supported, but not included by default.

tip

The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv9 models, but may support other YOLO model architectures as well. See the models section for more information on downloading YOLO models for use in Frigate.

After placing the downloaded onnx model in your config folder, you can use the following configuration:

detectors:
  onnx:
    type: onnx

model:
  model_type: yolo-generic
  width: 320 # <--- should match the imgsize set during model export
  height: 320 # <--- should match the imgsize set during model export
  input_tensor: nchw
  input_dtype: float
  path: /config/model_cache/yolo.onnx
  labelmap_path: /labelmap/coco-80.txt

Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.

AMD/ROCm GPU detector

Setup

Support for AMD GPUs is provided using the ONNX detector. In order to utilize the AMD GPU for object detection use a frigate docker image with -rocm suffix, for example ghcr.io/blakeblackshear/frigate:stable-rocm.

Docker settings for GPU access

ROCm needs access to the /dev/kfd and /dev/dri devices. When docker or frigate is not run under root then also video (and possibly render and ssl/_ssl) groups should be added.

When running docker directly the following flags should be added for device access:

$ docker run --device=/dev/kfd --device=/dev/dri  \
    ...

When using Docker Compose:

services:
  frigate:
---
devices:
  - /dev/dri
  - /dev/kfd

For reference on recommended settings see running ROCm/pytorch in Docker.

Docker settings for overriding the GPU chipset

Your GPU might work just fine without any special configuration but in many cases they need manual settings. AMD/ROCm software stack comes with a limited set of GPU drivers and for newer or missing models you will have to override the chipset version to an older/generic version to get things working.

Also AMD/ROCm does not "officially" support integrated GPUs. It still does work with most of them just fine but requires special settings. One has to configure the HSA_OVERRIDE_GFX_VERSION environment variable. See the ROCm bug report for context and examples.

For the rocm frigate build there is some automatic detection:

gfx90c -> 9.0.0
gfx1031 -> 10.3.0
gfx1103 -> 11.0.0

If you have something else you might need to override the HSA_OVERRIDE_GFX_VERSION at Docker launch. Suppose the version you want is 9.0.0, then you should configure it from command line as:

$ docker run -e HSA_OVERRIDE_GFX_VERSION=9.0.0 \
    ...

When using Docker Compose:

services:
  frigate:

environment:
  HSA_OVERRIDE_GFX_VERSION: "9.0.0"

Figuring out what version you need can be complicated as you can't tell the chipset name and driver from the AMD brand name.

first make sure that rocm environment is running properly by running /opt/rocm/bin/rocminfo in the frigate container -- it should list both the CPU and the GPU with their properties
find the chipset version you have (gfxNNN) from the output of the rocminfo (see below)
use a search engine to query what HSA_OVERRIDE_GFX_VERSION you need for the given gfx name ("gfxNNN ROCm HSA_OVERRIDE_GFX_VERSION")
override the HSA_OVERRIDE_GFX_VERSION with relevant value
if things are not working check the frigate docker logs

Figuring out if AMD/ROCm is working and found your GPU

$ docker exec -it frigate /opt/rocm/bin/rocminfo

Figuring out your AMD GPU chipset version:

We unset the HSA_OVERRIDE_GFX_VERSION to prevent an existing override from messing up the result:

$ docker exec -it frigate /bin/bash -c '(unset HSA_OVERRIDE_GFX_VERSION && /opt/rocm/bin/rocminfo |grep gfx)'

ROCm Supported Models

See ONNX supported models for supported models, there are some caveats:

D-FINE models are not supported
YOLO-NAS models are known to not run well on integrated GPUs

ONNX

ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, ROCm, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.

info

If the correct build is used for your GPU then the GPU will be detected and used automatically.

AMD
- ROCm will automatically be detected and used with the ONNX detector in the -rocm Frigate image.
Intel
- OpenVINO will automatically be detected and used with the ONNX detector in the default Frigate image.
Nvidia
- Nvidia GPUs will automatically be detected and used with the ONNX detector in the -tensorrt Frigate image.
- Jetson devices will automatically be detected and used with the ONNX detector in the -tensorrt-jp6 Frigate image.

tip

When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming GPU resources are available. An example configuration would be:

detectors:
  onnx_0:
    type: onnx
  onnx_1:
    type: onnx

ONNX Supported Models

There is no default model provided, the following formats are supported:

YOLO-NAS

YOLO-NAS models are supported, but not included by default. See the models section for more information on downloading the YOLO-NAS model for use in Frigate.

After placing the downloaded onnx model in your config folder, you can use the following configuration:

detectors:
  onnx:
    type: onnx

model:
  model_type: yolonas
  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

YOLO (v3, v4, v7, v9)

YOLOv3, YOLOv4, YOLOv7, and YOLOv9 models are supported, but not included by default.

tip

After placing the downloaded onnx model in your config folder, you can use the following configuration:

detectors:
  onnx:
    type: onnx

model:
  model_type: yolo-generic
  width: 320 # <--- should match the imgsize set during model export
  height: 320 # <--- should match the imgsize set during model export
  input_tensor: nchw
  input_dtype: float
  path: /config/model_cache/yolo.onnx
  labelmap_path: /labelmap/coco-80.txt

Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.

YOLOx

YOLOx models are supported, but not included by default. See the models section for more information on downloading the YOLOx model for use in Frigate.

After placing the downloaded onnx model in your config folder, you can use the following configuration:

detectors:
  onnx:
    type: onnx

model:
  model_type: yolox
  width: 416 # <--- should match the imgsize set during model export
  height: 416 # <--- should match the imgsize set during model export
  input_tensor: nchw
  input_dtype: float_denorm
  path: /config/model_cache/yolox_tiny.onnx
  labelmap_path: /labelmap/coco-80.txt

Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.

RF-DETR

RF-DETR is a DETR based model. The ONNX exported models are supported, but not included by default. See the models section for more information on downloading the RF-DETR model for use in Frigate.

After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:

detectors:
  onnx:
    type: onnx

model:
  model_type: rfdetr
  width: 320
  height: 320
  input_tensor: nchw
  input_dtype: float
  path: /config/model_cache/rfdetr.onnx

D-FINE

D-FINE is a DETR based model. The ONNX exported models are supported, but not included by default. See the models section for more information on downloading the D-FINE model for use in Frigate.

After placing the downloaded onnx model in your config/model_cache folder, you can use the following configuration:

detectors:
  onnx:
    type: onnx

model:
  model_type: dfine
  width: 640
  height: 640
  input_tensor: nchw
  input_dtype: float
  path: /config/model_cache/dfine_m_obj2coco.onnx
  labelmap_path: /labelmap/coco-80.txt

Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.

CPU Detector (not recommended)

The CPU detector type runs a TensorFlow Lite model utilizing the CPU without hardware acceleration. It is recommended to use a hardware accelerated detector type instead for better performance. To configure a CPU based detector, set the "type" attribute to "cpu".

danger

The CPU detector is not recommended for general use. If you do not have GPU or Edge TPU hardware, using the OpenVINO Detector in CPU mode is often more efficient than using the CPU detector.

The number of threads used by the interpreter can be specified using the "num_threads" attribute, and defaults to 3.

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.

detectors:
  cpu1:
    type: cpu
    num_threads: 3
  cpu2:
    type: cpu
    num_threads: 3

model:
  path: "/custom_model.tflite"

When using CPU detectors, you can add one CPU detector per camera. Adding more detectors than the number of cameras should not improve performance.

Deepstack / CodeProject.AI Server Detector

The Deepstack / CodeProject.AI Server detector for Frigate allows you to integrate Deepstack and CodeProject.AI object detection capabilities into Frigate. CodeProject.AI and DeepStack are open-source AI platforms that can be run on various devices such as the Raspberry Pi, Nvidia Jetson, and other compatible hardware. It is important to note that the integration is performed over the network, so the inference times may not be as fast as native Frigate detectors, but it still provides an efficient and reliable solution for object detection and tracking.

Setup

To get started with CodeProject.AI, visit their official website to follow the instructions to download and install the AI server on your preferred device. Detailed setup instructions for CodeProject.AI are outside the scope of the Frigate documentation.

To integrate CodeProject.AI into Frigate, you'll need to make the following changes to your Frigate configuration file:

detectors:
  deepstack:
    api_url: http://<your_codeproject_ai_server_ip>:<port>/v1/vision/detection
    type: deepstack
    api_timeout: 0.1 # seconds

Replace <your_codeproject_ai_server_ip> and <port> with the IP address and port of your CodeProject.AI server.

To verify that the integration is working correctly, start Frigate and observe the logs for any error messages related to CodeProject.AI. Additionally, you can check the Frigate web interface to see if the objects detected by CodeProject.AI are being displayed and tracked properly.

Community Supported Detectors

MemryX MX3

This detector is available for use with the MemryX MX3 accelerator M.2 module. Frigate supports the MX3 on compatible hardware platforms, providing efficient and high-performance object detection.

See the installation docs for information on configuring the MemryX hardware.

To configure a MemryX detector, simply set the type attribute to memryx and follow the configuration guide below.

Configuration

To configure the MemryX detector, use the following example configuration:

Single PCIe MemryX MX3

detectors:
  memx0:
    type: memryx
    device: PCIe:0

Multiple PCIe MemryX MX3 Modules

detectors:
  memx0:
    type: memryx
    device: PCIe:0

  memx1:
    type: memryx
    device: PCIe:1

  memx2:
    type: memryx
    device: PCIe:2

Supported Models

MemryX .dfp models are automatically downloaded at runtime, if enabled, to the container at /memryx_models/model_folder/.

YOLO-NAS

The YOLO-NAS model included in this detector is downloaded from the Models Section and compiled to DFP with mx_nc.

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.

Configuration

Below is the recommended configuration for using the YOLO-NAS (small) model with the MemryX detector:

detectors:
  memx0:
    type: memryx
    device: PCIe:0

model:
  model_type: yolonas
  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)

YOLOv9

The YOLOv9s model included in this detector is downloaded from the original GitHub like in the Models Section and compiled to DFP with mx_nc.

Configuration

Below is the recommended configuration for using the YOLOv9 (small) model with the MemryX detector:

detectors:
  memx0:
    type: memryx
    device: PCIe:0

model:
  model_type: yolo-generic   
  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)
          # └── yolov9_post.onnx    (optional; only if the model includes a cropped post-processing network)

YOLOX

The model is sourced from the OpenCV Model Zoo and precompiled to DFP.

Configuration

Below is the recommended configuration for using the YOLOX (small) model with the MemryX detector:

detectors:
  memx0:
    type: memryx
    device: PCIe:0

model:
  model_type: yolox
  width: 640
  height: 640
  input_tensor: nchw
  input_dtype: float_denorm
  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/yolox.zip
          # The .zip file must contain:
          # ├── yolox.dfp          (a file ending with .dfp)

SSDLite MobileNet v2

The model is sourced from the OpenMMLab Model Zoo and has been converted to DFP.

Configuration

Below is the recommended configuration for using the SSDLite MobileNet v2 model with the MemryX detector:

detectors:
  memx0:
    type: memryx
    device: PCIe:0

model:
  model_type: ssd
  width: 320
  height: 320
  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/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)

Using a Custom Model

To use your own model:

Package your compiled model into a .zip file.
The .zip must contain the compiled .dfp file.
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.
Bind-mount the .zip file into the container and specify its path using model.path in your config.
Update the labelmap_path to match your custom model's labels.

For detailed instructions on compiling models, refer to the MemryX Compiler docs and Tutorials.

  # The detector automatically selects the default model if nothing is provided in the config.
  #
  # Optionally, you can specify a local model path as a .zip file to override the default.
  # If a local path is provided and the file exists, it will be used instead of downloading.
  #
  # Example:
  # 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)

NVidia TensorRT Detector

Nvidia Jetson devices may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the -tensorrt-jp6 tag suffix, e.g. ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6. This detector is designed to work with Yolo models for object detection.

Generate Models

The model used for TensorRT must be preprocessed on the same hardware platform that they will run on. This means that each user must run additional setup to generate a model file for the TensorRT library. A script is included that will build several common models.

The Frigate image will generate model files during startup if the specified model is not found. Processed models are stored in the /config/model_cache folder. Typically the /config path is mapped to a directory on the host already and the model_cache does not need to be mapped separately unless the user wants to store it in a different location on the host.

By default, no models will be generated, but this can be overridden by specifying the YOLO_MODELS environment variable in Docker. One or more models may be listed in a comma-separated format, and each one will be generated. Models will only be generated if the corresponding {model}.trt file is not present in the model_cache folder, so you can force a model to be regenerated by deleting it from your Frigate data folder.

If you have a Jetson device with DLAs (Xavier or Orin), you can generate a model that will run on the DLA by appending -dla to your model name, e.g. specify YOLO_MODELS=yolov7-320-dla. The model will run on DLA0 (Frigate does not currently support DLA1). DLA-incompatible layers will fall back to running on the GPU.

If your GPU does not support FP16 operations, you can pass the environment variable USE_FP16=False to disable it.

Specific models can be selected by passing an environment variable to the docker run command or in your docker-compose.yml file. Use the form -e YOLO_MODELS=yolov4-416,yolov4-tiny-416 to select one or more model names. The models available are shown below.

Available Models

An example docker-compose.yml fragment that converts the yolov4-608 and yolov7x-640 models would look something like this:

frigate:
  environment:
    - YOLO_MODELS=yolov7-320,yolov7x-640
    - USE_FP16=false

Configuration Parameters

The TensorRT detector can be selected by specifying tensorrt as the model type. The GPU will need to be passed through to the docker container using the same methods described in the Hardware Acceleration section. If you pass through multiple GPUs, you can select which GPU is used for a detector with the device configuration parameter. The device parameter is an integer value of the GPU index, as shown by nvidia-smi within the container.

The TensorRT detector uses .trt model files that are located in /config/model_cache/tensorrt by default. These model path and dimensions used will depend on which model you have generated.

Use the config below to work with generated TRT models:

detectors:
  tensorrt:
    type: tensorrt
    device: 0 #This is the default, select the first GPU

model:
  path: /config/model_cache/tensorrt/yolov7-320.trt
  labelmap_path: /labelmap/coco-80.txt
  input_tensor: nchw
  input_pixel_format: rgb
  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

Rockchip platform

Hardware accelerated object detection is supported on the following SoCs:

RK3562
RK3566
RK3568
RK3576
RK3588

This implementation uses the Rockchip's RKNN-Toolkit2, version v2.3.2.

tip

When using many cameras one detector may not be enough to keep up. Multiple detectors can be defined assuming NPU resources are available. An example configuration would be:

detectors:
  rknn_0:
    type: rknn
    num_cores: 0
  rknn_1:
    type: rknn
    num_cores: 0

Prerequisites

Make sure to follow the Rockchip specific installation instructions.

tip

You can get the load of your NPU with the following command:

$ cat /sys/kernel/debug/rknpu/load
>> NPU load:  Core0:  0%, Core1:  0%, Core2:  0%,

RockChip Supported Models

This config.yml shows all relevant options to configure the detector and explains them. All values shown are the default values (except for two). Lines that are required at least to use the detector are labeled as required, all other lines are optional.

detectors: # required
  rknn: # required
    type: rknn # required
    # number of NPU cores to use
    # 0 means choose automatically
    # increase for better performance if you have a multicore NPU e.g. set to 3 on rk3588
    num_cores: 0

The inference time was determined on a rk3588 with 3 NPU cores.

Model	Size in mb	Inference time in ms
deci-fp16-yolonas_s	24	25
deci-fp16-yolonas_m	62	35
deci-fp16-yolonas_l	81	45
frigate-fp16-yolov9-t	6	35
rock-i8-yolox_nano	3	14
rock-i8_yolox_tiny	6	18

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.

YOLO-NAS

model: # required
  # 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

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

YOLO (v9)

model: # required
  # 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

YOLOx

model: # required
  # 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

Converting your own onnx model to rknn format

To convert a onnx model to the rknn format using the rknn-toolkit2 you have to:

Place one ore more models in onnx format in the directory config/model_cache/rknn_cache/onnx on your docker host (this might require sudo privileges).
Save the configuration file under config/conv2rknn.yaml (see below for details).
Run docker exec <frigate_container_id> python3 /opt/conv2rknn.py. If the conversion was successful, the rknn models will be placed in config/model_cache/rknn_cache.

This is an example configuration file that you need to adjust to your specific onnx model:

soc: ["rk3562", "rk3566", "rk3568", "rk3576", "rk3588"]
quantization: false

output_name: "{input_basename}"

config:
  mean_values: [[0, 0, 0]]
  std_values: [[255, 255, 255]]
  quant_img_RGB2BGR: true

Explanation of the paramters:

soc: A list of all SoCs you want to build the rknn model for. If you don't specify this parameter, the script tries to find out your SoC and builds the rknn model for this one.
quantization: true: 8 bit integer (i8) quantization, false: 16 bit float (fp16). Default: false.
output_name: The output name of the model. The following variables are available:
- quant: "i8" or "fp16" depending on the config
- input_basename: the basename of the input model (e.g. "my_model" if the input model is calles "my_model.onnx")
- soc: the SoC this model was build for (e.g. "rk3588")
- tk_version: Version of rknn-toolkit2 (e.g. "2.3.0")
- example: Specifying output_name = "frigate-{quant}-{input_basename}-{soc}-v{tk_version}" could result in a model called frigate-i8-my_model-rk3588-v2.3.0.rknn.
config: Configuration passed to rknn-toolkit2 for model conversion. For an explanation of all available parameters have a look at section "2.2. Model configuration" of this manual.

DeGirum

DeGirum is a detector that can use any type of hardware listed on their website. DeGirum can be used with local hardware through a DeGirum AI Server, or through the use of @local. You can also connect directly to DeGirum's AI Hub to run inferences. Please Note: This detector cannot be used for commercial purposes.

Configuration

AI Server Inference

Before starting with the config file for this section, you must first launch an AI server. DeGirum has an AI server ready to use as a docker container. Add this to your docker-compose.yml to get started:

degirum_detector:
    container_name: degirum
    image: degirum/aiserver:latest
    privileged: true
    ports:
      - "8778:8778"

All supported hardware will automatically be found on your AI server host as long as relevant runtimes and drivers are properly installed on your machine. Refer to DeGirum's docs site if you have any trouble.

Once completed, changing the config.yml file is simple.

degirum_detector:
    type: degirum
    location: degirum # Set to service name (degirum_detector), container_name (degirum), or a host:port (192.168.29.4:8778)
    zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start. If you aren't pulling a model from the AI Hub, leave this and 'token' blank.
    token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server

Setting up a model in the config.yml is similar to setting up an AI server. You can set it to:

A model listed on the AI Hub, given that the correct zoo name is listed in your detector
- If this is what you choose to do, the correct model will be downloaded onto your machine before running.
A local directory acting as a zoo. See DeGirum's docs site for more information.
A path to some model.json.

model:
    path: ./mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1 # directory to model .json and file
    width: 300 # width is in the model name as the first number in the "int"x"int" section
    height: 300 # height is in the model name as the second number in the "int"x"int" section
    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here

Local Inference

It is also possible to eliminate the need for an AI server and run the hardware directly. The benefit of this approach is that you eliminate any bottlenecks that occur when transferring prediction results from the AI server docker container to the frigate one. However, the method of implementing local inference is different for every device and hardware combination, so it's usually more trouble than it's worth. A general guideline to achieve this would be:

Ensuring that the frigate docker container has the runtime you want to use. So for instance, running @local for Hailo means making sure the container you're using has the Hailo runtime installed.
To double check the runtime is detected by the DeGirum detector, make sure the degirum sys-info command properly shows whatever runtimes you mean to install.
Create a DeGirum detector in your config.yml file.

degirum_detector:
    type: degirum
    location: "@local" # For accessing AI Hub devices and models
    zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
    token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the [AI Hub](https://hub.degirum.com). This can be left blank if you're pulling a model from the public zoo and running inferences on your local hardware using @local or a local DeGirum AI Server

Once degirum_detector is setup, you can choose a model through 'model' section in the config.yml file.

model:
    path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
    width: 300 # width is in the model name as the first number in the "int"x"int" section
    height: 300 # height is in the model name as the second number in the "int"x"int" section
    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here

AI Hub Cloud Inference

If you do not possess whatever hardware you want to run, there's also the option to run cloud inferences. Do note that your detection fps might need to be lowered as network latency does significantly slow down this method of detection. For use with Frigate, we highly recommend using a local AI server as described above. To set up cloud inferences,

Sign up at DeGirum's AI Hub.
Get an access token.
Create a DeGirum detector in your config.yml file.

degirum_detector:
    type: degirum
    location: "@cloud" # For accessing AI Hub devices and models
    zoo: degirum/public # DeGirum's public model zoo. Zoo name should be in format "workspace/zoo_name". degirum/public is available to everyone, so feel free to use it if you don't know where to start.
    token: dg_example_token # For authentication with the AI Hub. Get this token through the "tokens" section on the main page of the (AI Hub)[https://hub.degirum.com).

Once degirum_detector is setup, you can choose a model through 'model' section in the config.yml file.

model:
    path: mobilenet_v2_ssd_coco--300x300_quant_n2x_orca1_1
    width: 300 # width is in the model name as the first number in the "int"x"int" section
    height: 300 # height is in the model name as the second number in the "int"x"int" section
    input_pixel_format: rgb/bgr # look at the model.json to figure out which to put here

Models

Some model types are not included in Frigate by default.

Downloading Models

Here are some tips for getting different model types

Downloading D-FINE Model

To export as ONNX:

Clone: https://github.com/Peterande/D-FINE and install all dependencies.
Select and download a checkpoint from the readme.
Modify line 58 of tools/deployment/export_onnx.py and change batch size to 1: data = torch.rand(1, 3, 640, 640)
Run the export, making sure you select the right config, for your checkpoint.

Example:

python3 tools/deployment/export_onnx.py -c configs/dfine/objects365/dfine_hgnetv2_m_obj2coco.yml -r output/dfine_m_obj2coco.pth

tip

Model export has only been tested on Linux (or WSL2). Not all dependencies are in requirements.txt. Some live in the deployment folder, and some are still missing entirely and must be installed manually.

Make sure you change the batch size to 1 before exporting.

Download RF-DETR Model

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.

docker build . --build-arg MODEL_SIZE=Nano --output . -f- <<'EOF'
FROM python:3.11 AS build
RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
WORKDIR /rfdetr
RUN uv pip install --system rfdetr onnx onnxruntime onnxsim onnx-graphsurgeon
ARG MODEL_SIZE
RUN python3 -c "from rfdetr import RFDETR${MODEL_SIZE}; x = RFDETR${MODEL_SIZE}(resolution=320); x.export()"
FROM scratch
ARG MODEL_SIZE
COPY --from=build /rfdetr/output/inference_model.onnx /rfdetr-${MODEL_SIZE}.onnx
EOF

Downloading YOLO-NAS Model

You can build and download a compatible model with pre-trained weights using this notebook which can be run directly in Google Colab.

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.

Downloading YOLO Models

YOLOx

YOLOx models can be downloaded from the YOLOx repo.

YOLOv3, YOLOv4, and YOLOv7

To export as ONNX:

git clone https://github.com/NateMeyer/tensorrt_demos
cd tensorrt_demos/yolo
./download_yolo.sh
python3 yolo_to_onnx.py -m yolov7-320

YOLOv9

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 in the first line to the model size you would like to convert (available sizes are t, s, m, c, and e).

docker build . --build-arg MODEL_SIZE=t --output . -f- <<'EOF'
FROM python:3.11 AS build
RUN apt-get update && apt-get install --no-install-recommends -y libgl1 && rm -rf /var/lib/apt/lists/*
COPY --from=ghcr.io/astral-sh/uv:0.8.0 /uv /bin/
WORKDIR /yolov9
ADD https://github.com/WongKinYiu/yolov9.git .
RUN uv pip install --system -r requirements.txt
RUN uv pip install --system onnx onnxruntime onnx-simplifier>=0.4.1
ARG MODEL_SIZE
ADD https://github.com/WongKinYiu/yolov9/releases/download/v0.1/yolov9-${MODEL_SIZE}-converted.pt yolov9-${MODEL_SIZE}.pt
RUN sed -i "s/ckpt = torch.load(attempt_download(w), map_location='cpu')/ckpt = torch.load(attempt_download(w), map_location='cpu', weights_only=False)/g" models/experimental.py
RUN python3 export.py --weights ./yolov9-${MODEL_SIZE}.pt --imgsz 320 --simplify --include onnx
FROM scratch
ARG MODEL_SIZE
COPY --from=build /yolov9/yolov9-${MODEL_SIZE}.onnx /
EOF

Officially Supported Detectors

Edge TPU Detector​

Single USB Coral​

Multiple USB Corals​

Native Coral (Dev Board)​

Single PCIE/M.2 Coral​

Multiple PCIE/M.2 Corals​

Mixing Corals​

Hailo-8​

Configuration​

YOLO​

SSD​

Custom Models​

OpenVINO Detector​

OpenVINO Supported Models​

SSDLite MobileNet v2​

YOLOX​

YOLO-NAS​

YOLO (v3, v4, v7, v9)​

RF-DETR​

D-FINE​

Apple Silicon detector​

Setup​

Configuration​

Apple Silicon Supported Models​

YOLO (v3, v4, v7, v9)​

AMD/ROCm GPU detector​

Setup​

Docker settings for GPU access​

Docker settings for overriding the GPU chipset​

Figuring out if AMD/ROCm is working and found your GPU​

Figuring out your AMD GPU chipset version:​

ROCm Supported Models​

ONNX​

ONNX Supported Models​

YOLO-NAS​

YOLO (v3, v4, v7, v9)​

YOLOx​

RF-DETR​

D-FINE​

CPU Detector (not recommended)​

Deepstack / CodeProject.AI Server Detector​

Setup​

Community Supported Detectors

MemryX MX3​

Configuration​

Single PCIe MemryX MX3​

Multiple PCIe MemryX MX3 Modules​

Supported Models​

YOLO-NAS​

Configuration​

YOLOv9​

Configuration​

YOLOX​

Configuration​

SSDLite MobileNet v2​

Configuration​

Using a Custom Model​

NVidia TensorRT Detector​

Generate Models​

Configuration Parameters​

Rockchip platform​

Prerequisites​

RockChip Supported Models​

YOLO-NAS​

YOLO (v9)​

YOLOx​

Converting your own onnx model to rknn format​

DeGirum​

Configuration​

AI Server Inference​

Local Inference​

AI Hub Cloud Inference​

Models

Downloading Models​

Downloading D-FINE Model​

Download RF-DETR Model​

Downloading YOLO-NAS Model​

Downloading YOLO Models​

YOLOx​

Edge TPU Detector

Single USB Coral

Multiple USB Corals

Native Coral (Dev Board)

Single PCIE/M.2 Coral

Multiple PCIE/M.2 Corals

Mixing Corals

Hailo-8

Configuration

YOLO

SSD

Custom Models

OpenVINO Detector

OpenVINO Supported Models

SSDLite MobileNet v2

YOLOX

YOLO-NAS

YOLO (v3, v4, v7, v9)

RF-DETR

D-FINE

Apple Silicon detector

Setup

Configuration

Apple Silicon Supported Models

YOLO (v3, v4, v7, v9)

AMD/ROCm GPU detector

Setup

Docker settings for GPU access

Docker settings for overriding the GPU chipset

Figuring out if AMD/ROCm is working and found your GPU

Figuring out your AMD GPU chipset version:

ROCm Supported Models

ONNX

ONNX Supported Models

YOLO-NAS

YOLO (v3, v4, v7, v9)

YOLOx

RF-DETR

D-FINE

CPU Detector (not recommended)

Deepstack / CodeProject.AI Server Detector

Setup

MemryX MX3

Configuration

Single PCIe MemryX MX3

Multiple PCIe MemryX MX3 Modules

Supported Models

YOLO-NAS

Configuration

YOLOv9

Configuration

YOLOX

Configuration

SSDLite MobileNet v2

Configuration

Using a Custom Model

NVidia TensorRT Detector

Generate Models

Configuration Parameters

Rockchip platform

Prerequisites

RockChip Supported Models

YOLO-NAS

YOLO (v9)

YOLOx

Converting your own onnx model to rknn format

DeGirum

Configuration

AI Server Inference

Local Inference

AI Hub Cloud Inference

Downloading Models

Downloading D-FINE Model

Download RF-DETR Model

Downloading YOLO-NAS Model

Downloading YOLO Models

YOLOx

YOLOv3, YOLOv4, and YOLOv7

YOLOv9