This is the implementation of "Scaled-YOLOv4: Scaling Cross Stage Partial Network" using PyTorch framwork.
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2021.05.21 Due to unknown issue some people can not reproduce the performance in paper and I can not reproduce the issue#89, I update the codebase. But it will makes the reproduce performance becomes better than paper (47.8 AP -> 48.7 AP).
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2020.11.16 Now supported by Darknet.
yolov4-csp.cfgyolov4-csp.weights
# 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:20.11-py3
# apt install required packages
apt update
apt install -y zip htop screen libgl1-mesa-glx
# pip install required packages
pip install seaborn thop
# install mish-cuda if you want to use mish activation
# https://github.com/thomasbrandon/mish-cuda
# https://github.com/JunnYu/mish-cuda
cd /
git clone https://github.com/JunnYu/mish-cuda
cd mish-cuda
python setup.py build install
# go to code folder
cd /yolo
old weights
# download yolov4-csp.weights and put it in /yolo/weights/ folder.
python test.py --img 640 --conf 0.001 --iou 0.65 --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 ] = 0.48656
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.67002
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.52739
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.33082
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.54036
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.62107
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.37197
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.61211
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.66544
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.49676
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.72018
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.80528
old 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
# 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/train/yolov4-csp/weights/.
python train.py --device 0 --batch-size 16 --data coco.yaml --cfg yolov4-csp.cfg --weights 'runs/train/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
@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}
}