Deploy YoloV5
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GitHub - ultralytics/yolov5: YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
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GitHub - freshtechyy/ONNX-Runtime-GPU-image-classifciation-example
Python yolov5 flask docker file
yolov5-flask/Dockerfile at master · robmarkcole/yolov5-flask · GitHub
Rest api https://github.com/prince776/yt-projects/tree/master/cppREST
curl -X POST -F image=@image.jpg 'http://localhost:5000/v1/object-detection/yolov5'
- create docker files
- using COCO model in C++
- check C++ code deeper
- update presentation
- endpoints
- use case
Build docker commands:
# build
docker build -t yolov5-flask dockerfiles/restapi-python.Dockerfile
# run
docker run -p 5000:5000 yolov5-flask:latest
Run RestAPI client:
python restapi-client.py --image_path="../demo.jpg" --port=5000 --conf=0.4
Install NVidia toolkit
sudo apt-get install -y nvidia-container-toolkit
- Add
sudo apt install nvidia-cuda-toolkit
Export .pt file to .onnx https://colab.research.google.com/drive/1V-F3erKkPun-vNn28BoOc6ENKmfo8kDh?usp=sharing#scrollTo=12t-V70DmpSl
cmake .. && make && ./src/main --model_path ../yolov5s6.onnx --class_names ../coco.names --gpu --port 3000