Frigate Detect
Charlesetta Blare
Frigate provides the following builtin detector types: cpu, edgetpu, openvino, tensorrt, and rknn. 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".
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.
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.
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. 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. 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.
This detector also supports YOLOX. 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:
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.
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, e.g. ghcr.io/blakeblackshear/frigate:stable-tensorrt. This detector is designed to work with Yolo models for object detection.
The TensorRT detector uses the 12.x series of CUDA libraries which have minor version compatibility. The minimum driver version on the host system must be >=530. 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.
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.
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, the yolov7-320 model 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. To select no model generation, set the variable to an empty string, YOLO_MODELS="". 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.
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.
If you have multiple GPUs passed through to Frigate, you can specify which one to use for the model conversion. The conversion script will use the first visible GPU, however in systems with mixed GPU models you may not want to use the default index for object detection. Add the TRT_MODEL_PREP_DEVICE environment variable to select a specific GPU.
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 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.
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 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.
Traditional NVRs can require hours of fine tuning to reduce false positive rates because they rely on simple motion detection. By offloading object detection to the Google Coral TPU, even modest hardware can run advanced analysis to determine if the motion is actually a person, car, or other object of interest. With Frigate's local processing, there is no need to pay for your personal camera footage to be sent to the cloud for analysis.
Frigate tracks objects in real-time and can determine the exact moment a person starts walking up your front steps or when a car enters your driveway. Refine your notifications based on precise locations.
Give your home eyes by integrating object detection into Home Assistant, OpenHab, NodeRed, or anything with MQTT support. Frigate integrates directly into Home Assistant's media browser, provides low latency camera entities, and exposes real-time sensors and switches to power automations and notifications to your heart's content.
Note that you do not need to use HomeAssistant or MQTT in order to use or try Frigate, it can run as a standalone insatnce if you like. Frigate also comes with its own web interface which is very good, and I run this full-screen/kiosk mode on one of my monitors.
Setup and trial Frigate: setting up Frigate was easy, I went for Ubuntu on my host and installed Docker on that, then configured Frigate and MQTT containers to communicate. These are both simply declared in the Frigate config like this:
Integrate with HomeAssistant : Added the HomeAssistant Docker instance to my host, then ran and configured MQTT container for Frigate then configured Frigate + HomeAssistant to work together. This was done by first installing HACS in HA, then using the Frigate Integration as explained here:
-assistant/
I can now open HomeAssistant on my phone from anywhere in the World and view a dashboard that has live feeds from my CCTV cameras at home. I have also set it up to show recently detected objects from certain cameras too.
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