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 GPU device. see parallels.
4. Adjust other parameters.

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 official_yolov3_weights_pytorch.pth to wegihts folder in this project.

Start evaluate

cd evaluate
python eval_coco.py params.py

Quick test

pretrained weights

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

Start test

cd test
python test_images.py params.py

You can got result images in output folder.

Measure FPS

pretrained weights

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

Start test

cd test
python test_fps.py params.py

Results

• 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.

Credit

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

Reference

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