Full implementation of YOLOv3 in PyTorch.

[Paper]
[Original Implementation]

• Implement YOLOv3 and darknet53 without original darknet cfg parser.
• It is easy to custom your backbone network. Such as resnet, densenet...

• pytorch >= 0.4.0
• python >= 3.6.0

git clone https://github.com/BobLiu20/YOLOv3_PyTorch.git
cd YOLOv3_PyTorch
pip3 install -r requirements.txt --user

cd data/
bash get_coco_dataset.sh

1. See weights readme for detail.
2. Download pretrained backbone wegiths from Google Drive or Baidu Drive
3. Move downloaded file darknet53_weights_pytorch.pth to wegihts folder in this project.

1. Review config file training/params.py
2. Replace YOUR_WORKING_DIR to your working directory. Use for save model and tmp file.
3. Adjust your GPU device. see parallels.
4. Adjust other parameters.

cd training
python training.py params.py

#  please install tensorboard in first
python -m tensorboard.main --logdir=YOUR_WORKING_DIR   

1. See weights readme for detail.
2. Download pretrained yolo3 full wegiths from Google Drive or Baidu Drive
3. Move downloaded file official_yolov3_weights_pytorch.pth to wegihts folder in this project.

cd evaluate
python eval_coco.py params.py

Please download pretrained weights official_yolov3_weights_pytorch.pth or use yourself checkpoint.

cd test
python test_images.py params.py

You can got result images in output folder.

Please download pretrained weights official_yolov3_weights_pytorch.pth or use yourself checkpoint.

cd test
python test_fps.py params.py

• Test in TitanX GPU with different input size and batch size.
• Keep in mind this is a full test in YOLOv3. Not only backbone but also yolo layer and NMS.

@article{yolov3,
	title={YOLOv3: An Incremental Improvement},
	author={Redmon, Joseph and Farhadi, Ali},
	journal = {arXiv},
	year={2018}
}

• darknet
• PyTorch-YOLOv3: Thanks for YOLO loss code