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Detectors

Frigate provides the following builtin detector types: cpu, edgetpu, openvino, 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.

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".

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
    model:
      path: "/custom_model.tflite"
  cpu2:
    type: cpu
    num_threads: 3

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

Edge-TPU Detector​

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

The EdgeTPU 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.

Single USB Coral​

detectors:
  coral:
    type: edgetpu
    device: usb
    model:
      path: "/custom_model.tflite"

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: ""

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

OpenVINO Detector​

The OpenVINO detector type runs an OpenVINO IR model on Intel CPU, GPU and 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. Other supported devices could be AUTO, CPU, GPU, MYRIAD, etc. If not specified, the default OpenVINO device will be selected by the AUTO plugin.

OpenVINO is supported on 6th Gen Intel platforms (Skylake) and newer. A supported Intel platform is required to use the GPU device with OpenVINO. The MYRIAD device may be run on any platform, including Arm devices. For detailed system requirements, see OpenVINO System Requirements

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 model.

detectors:
  ov:
    type: openvino
    device: AUTO
    model:
      path: /openvino-model/ssdlite_mobilenet_v2.xml

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

This detector also supports some YOLO variants: YOLOX, YOLOv5, and YOLOv8 specifically. Other YOLO variants are not officially supported/tested. Frigate does not come with any yolo models preloaded, so you will need to supply your own models. This detector has been verified to work with the yolox_tiny model from Intel's Open Model Zoo. You can follow these instructions to retrieve the OpenVINO-compatible yolox_tiny model. Make sure that the model input dimensions match the width and height parameters, and model_type is set accordingly. See Full Configuration Reference for a list of possible model_type options. Below is an example of how yolox_tiny can be used in Frigate:

detectors:
  ov:
    type: openvino
    device: AUTO
    model:
      path: /path/to/yolox_tiny.xml

model:
  width: 416
  height: 416
  input_tensor: nchw
  input_pixel_format: bgr
  model_type: yolox
  labelmap_path: /path/to/coco_80cl.txt

Intel NCS2 VPU and Myriad X Setup​

Intel produces a neural net inference accelleration chip called Myriad X. This chip was sold in their Neural Compute Stick 2 (NCS2) which has been discontinued. If intending to use the MYRIAD device for accelleration, additional setup is required to pass through the USB device. The host needs a udev rule installed to handle the NCS2 device.

sudo usermod -a -G users "$(whoami)"
cat <<EOF > 97-myriad-usbboot.rules
SUBSYSTEM=="usb", ATTRS{idProduct}=="2485", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
SUBSYSTEM=="usb", ATTRS{idProduct}=="f63b", ATTRS{idVendor}=="03e7", GROUP="users", MODE="0666", ENV{ID_MM_DEVICE_IGNORE}="1"
EOF
sudo cp 97-myriad-usbboot.rules /etc/udev/rules.d/
sudo udevadm control --reload-rules
sudo udevadm trigger

Additionally, the Frigate docker container needs to run with the following configuration:

--device-cgroup-rule='c 189:\* rmw' -v /dev/bus/usb:/dev/bus/usb

or in your compose file:

device_cgroup_rules:
  - "c 189:* rmw"
volumes:
  - /dev/bus/usb:/dev/bus/usb

NVidia TensorRT Detector​

NVidia GPUs 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 tag suffix. This detector is designed to work with Yolo models for object detection.

Minimum Hardware Support​

The TensorRT detector uses the 11.x series of CUDA libraries which have minor version compatibility. The minimum driver version on the host system must be >=450.80.02. Also the GPU must support a Compute Capability of 5.0 or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.

TODO: NVidia claims support on compute 3.5 and 3.7, but marks it as deprecated. This would have some, but not all, Kepler GPUs as possibly working. This needs testing before making any claims of support.

To use the TensorRT detector, make sure your host system has the nvidia-container-runtime installed to pass through the GPU to the container and the host system has a compatible driver installed for your GPU.

There are improved capabilities in newer GPU architectures that TensorRT can benefit from, such as INT8 operations and Tensor cores. The features compatible with your hardware will be optimized when the model is converted to a trt file. Currently the script provided for generating the model provides a switch to enable/disable FP16 operations. If you wish to use newer features such as INT8 optimization, more work is required.

Compatibility References:​

NVIDIA TensorRT Support Matrix

NVIDIA CUDA Compatibility

NVIDIA GPU Compute Capability

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 provided that will build several common models.

To generate model files, create a new folder to save the models, download the script, and launch a docker container that will run the script.

mkdir trt-models
wget https://github.com/blakeblackshear/frigate/raw/master/docker/tensorrt_models.sh
chmod +x tensorrt_models.sh
docker run --gpus=all --rm -it -v `pwd`/trt-models:/tensorrt_models -v `pwd`/tensorrt_models.sh:/tensorrt_models.sh nvcr.io/nvidia/tensorrt:22.07-py3 /tensorrt_models.sh

The trt-models folder can then be mapped into your Frigate container as trt-models and the models referenced from the config.

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

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

yolov3-288
yolov3-416
yolov3-608
yolov3-spp-288
yolov3-spp-416
yolov3-spp-608
yolov3-tiny-288
yolov3-tiny-416
yolov4-288
yolov4-416
yolov4-608
yolov4-csp-256
yolov4-csp-512
yolov4-p5-448
yolov4-p5-896
yolov4-tiny-288
yolov4-tiny-416
yolov4x-mish-320
yolov4x-mish-640
yolov7-tiny-288
yolov7-tiny-416
yolov7-640
yolov7-320
yolov7x-640
yolov7x-320

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 /trt-models/ by default. These model file path and dimensions used will depend on which model you have generated.

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

model:
  path: /trt-models/yolov7-tiny-416.trt
  input_tensor: nchw
  input_pixel_format: rgb
  width: 416
  height: 416

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.