YOLOv3

Full implementation of YOLOv3 in PyTorch.

Overview

YOLOv3: An Incremental Improvement

[Paper]
[Original Implementation]

Why this project

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

Installation

Environment

• pytorch >= 0.4.0
• python >= 3.6.0

Get code

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

Download COCO dataset

cd data/
bash get_coco_dataset.sh

Training

Download pretrained weights

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.

Modify training parameters

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 lr, parallels and so on.

Start training

cd training
python training.py params.py

Option: Visualizing training

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

Evaluate

Download pretrained weights

1. See weights readme for detail.
2. Download pretrained yolo3 full wegiths from Google Drive or Baidu Drive
3. Move downloaded file yolov3_weights_pytorch.pth to wegihts folder in this project.

Start evaluate

cd evaluate
python eval.py params.py

Results

Model	mAP (min. 50 IoU)	weights file
YOLOv3 (paper)	57.9
YOLOv3 (convert from paper)	58.18	official_yolov3_weights_pytorch.pth
YOLOv3 (train best model)	59.66	yolov3_weights_pytorch.pth

Credit

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

Reference

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