CPU Deployment Support

README.md

@@ -1,20 +1,27 @@
-# Scaled-YOLOv4
+# Welcome
 
-This is the implementation of "[Scaled-YOLOv4: Scaling Cross Stage Partial Network](https://arxiv.org/abs/2011.08036)".
+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](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-csp)
 
-![Scaled-YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4/blob/main/figure/scaled-yolov4.png)
+Implementation is in `Pytorch` but you can use the `DarkNet`. Please refer to the original documentation.
 
-* [YOLOv4-CSP](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-csp)
-* [YOLOv4-tiny](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-tiny)
-* [YOLOv4-large](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-large)
 
-## Citation
+# Why Fork?
 
-```
-@article{wang2020scaled,
-  title={{Scaled-YOLOv4}: Scaling Cross Stage Partial Network},
-  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
-  journal={arXiv preprint arXiv:2011.08036},
-  year={2020}
-}
-```
+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
+5. On your terminal, do `git checkout yolov4-csp`
+6. Store your model weights somewhere and edit the `weights` variable inside `API_deploy_CPU.py` (Optional)
+7. run `python API_deploy_CPU.py`. It'll deploy a very very basic model on `flask`
+8. Input the path to weights on terminal
+9. Use `postman` or `requests` module to send the request at `localhost:5000/predict`. Check the port number first.
+10. 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.