chris/frigate
1
0
Fork 0
forked from Github/frigate
Code Pull Requests Packages Activity

Nvidia Jetson ffmpeg + TensorRT support (#6458)

Browse Source 
* Non-Jetson changes

Required for later commits:
- Allow base image to be overridden (and don't assume its WORKDIR)
- Ensure python3.9
- Map hwaccel decode presets as strings instead of lists
Not required:
- Fix existing documentation
- Simplify hwaccel scale logic

* Prepare for multi-arch tensorrt build

* Add tensorrt images for Jetson boards

* Add Jetson ffmpeg hwaccel

* Update docs

* Add CODEOWNERS

* CI

* Change default model from yolov7-tiny-416 to yolov7-320

In my experience the tiny models perform markedly worse without being
much faster

* fixup! Update docs
This commit is contained in:
Andrew Reiter
2023-07-26 06:50:41 -04:00
committed by GitHub
parent 680198148b
commit a96a951e23
28 changed files with 567 additions and 139 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
docs/docs/configuration/object_detectors.md
View File

@@ -196,7 +196,9 @@ The model used for TensorRT must be preprocessed on the same hardware platform t
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.
To by default, the `yolov7-tiny-416` 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.
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.
If your GPU does not support FP16 operations, you can pass the environment variable `USE_FP16=False` to disable it.
@@ -252,11 +254,11 @@ detectors:
device: 0 #This is the default, select the first GPU
model:
path: /config/model_cache/tensorrt/yolov7-tiny-416.trt
path: /config/model_cache/tensorrt/yolov7-320.trt
input_tensor: nchw
input_pixel_format: rgb
width: 416
height: 416
width: 320
height: 320
```
## Deepstack / CodeProject.AI Server Detector