Scaled-YOLOv4: Scaling Cross Stage Partial Network is yolov4-csp. Actual produces erros on CPU so there is a bit modification. This is a small code to build API for newcomers. Please refer to this original link for Scaled Yolov4 AKA yolov4-csp

Implementation is in Pytorch but you can use the DarkNet. Please refer to the original documentation.

If you are here, it means that you must be looking for a way to Deploy Scaled Yolov4 model using CPU. Original implementation uses mishcuda on the model loading itself so it generates error. yolov4-csp is actually Scaled Yolov4.

Steps:

1. Clone this repo
2. Navigate to ScaledYOLOv4/
3. You'll find it empty because all work is done on the yolov4-csp branch
4. On your terminal, do git checkout yolov4-csp
5. Store your model weights somewhere and edit the weights variable inside API_deploy_CPU.py (Optional)
6. run python API_deploy_CPU.py. It'll deploy a very very basic model on flask
7. Input the path to weights on terminal
8. Use postman or requests module to send the request at localhost:5000/predict. Check the port number first.
9. Results returned are list of lists in the form of [ [x_min, y_min, x_max, y_ax, class, conf_score], [......], .....[...], ]

I can bet You've missed step No 4 ;)

Code for this API is built around a Single class model. Please change and tweak the code given in detect.py according to your needs.