Qt-deploy-YoloV5

Deploy YoloV5

Used repository

• GitHub - ultralytics/yolov5: YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

• GitHub - freshtechyy/ONNX-Runtime-GPU-image-classifciation-example

Tutorials

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 [email protected] 'http://localhost:5000/v1/object-detection/yolov5'

• create docker files
• using COCO model in C++
• check C++ code deeper
• update presentation
  • endpoints
  • use case

Docker

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

CPP docker TODO

• 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