INTRODUCTION

Thank you for your contribution. Here is my Chinese tutorial

https://github.com/Eric3911/ScaledYOLOv4/blob/master/readme_ch.md

SHOW

YOLOv4-CSP

This is the implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork.

• 2020.11.16 Now supported by Darknet. [yolo] new_coords=1

Installation

# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov4_csp -it -v your_coco_path/:/coco/ -v your_code_path/:/yolo --shm-size=64g nvcr.io/nvidia/pytorch:20.06-py3

# install mish-cuda, if you use different pytorch version, you could try https://github.com/JunnYu/mish-cuda
cd /
git clone https://github.com/thomasbrandon/mish-cuda
cd mish-cuda
python setup.py build install

# go to code folder
cd /yolo

Testing

yolov4-csp.weights

# download yolov4-csp.weights and put it in /yolo/weights/ folder.
python test.py --img 640 --conf 0.001 --batch 8 --device 0 --data coco.yaml --cfg models/yolov4-csp.cfg --weights weights/yolov4-csp.weights

You will get the results:

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.47827
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.66448
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.51928
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.30647
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.53106
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.61056
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.36823
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.60434
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.65795
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.48486
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.70892
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.79914

Training

# you can change batch size to fit your GPU RAM.
python train.py --device 0 --batch-size 16 --data coco.yaml --cfg yolov4-csp.cfg --weights '' --name yolov4-csp

For resume training:

# assume the checkpoint is stored in runs/exp0_yolov4-csp/weights/.
python train.py --device 0 --batch-size 16 --data coco.yaml --cfg yolov4-csp.cfg --weights 'runs/exp0_yolov4-csp/weights/last.pt' --name yolov4-csp --resume

If you want to use multiple GPUs for training

python -m torch.distributed.launch --nproc_per_node 4 train.py --device 0,1,2,3 --batch-size 64 --data coco.yaml --cfg yolov4-csp.cfg --weights '' --name yolov4-csp --sync-bn

Citation

@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}
}