administrator/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2026-04-15 11:32:09 +03:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Enhance YOLOv9 export instructions in documentation

Browse Source 
Updated YOLOv9 export command to include IMG_SIZE parameter and clarified model size options.
This commit is contained in:
laviddichterman 2025-09-08 18:35:56 -07:00 committed by GitHub
parent bdba7561d9
commit a7b539b36d
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
docs/docs/configuration/object_detectors.md
View File

@ -699,6 +699,7 @@ To verify that the integration is working correctly, start Frigate and observe t
# Community Supported Detectors
## 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
@ -716,7 +717,6 @@ If your GPU does not support FP16 operations, you can pass the environment varia
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.
<details>
<summary>Available Models</summary>
```
yolov3-288
@ -1030,10 +1030,10 @@ 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](https://github.com/WongKinYiu/yolov9#performance) you would like to convert (available sizes are `t`, `s`, `m`, `c`, and `e`).
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`).
```sh
docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=640 --output . -f- <<'EOF'
docker build . --build-arg MODEL_SIZE=t --build-arg IMG_SIZE=320 --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/