Full implementation of YOLO version3 in PyTorch, including training, evaluation, simple deployment(developing).
[Paper]
[Original Implementation]
Implement YOLOv3 and darknet53 without original darknet cfg parser.
It is easy to custom your backbone network. Such as resnet, densenet...
Also decide to develop custom structure (like grayscale pretrained model)
git clone https://github.com/zhanghanduo/yolo3_pytorch.git
cd YOLOv3_PyTorch
pip3 install -r requirements.txt --user
cd data/
bash get_coco_dataset.sh
Please visit BDD100K for details.
official_yolov3_weights_pytorch.pth
to weights
folder in this project.
training/params.py
YOUR_WORKING_DIR
to your working directory. Use for save model and tmp file.cd training
python training.py params.py
# please install tensorboard in first
python -m tensorboard.main --logdir=YOUR_WORKING_DIR
yolov3_weights_pytorch.pth
to wegihts
folder in this project.
cd evaluate
python eval.py params.py
python eval_coco.py params.py
@article{yolov3,
title={YOLOv3: An Incremental Improvement},
author={Redmon, Joseph and Farhadi, Ali},
journal = {arXiv},
year={2018}
}