Welcome

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.

Why Fork?

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 ;)

Note:

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.