/YOLOv3_PyTorch

Full implementation of YOLOv3 in PyTorch

Primary LanguagePython

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...
  • It is easy to reproduce result of paper using default parameters(~10 epochs).

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. (keep default to reproduce the results of paper after 10 epochs)
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 (our train 20 epochs) 59.66 yolov3_weights_pytorch.pth
YOLOv3 (our train 60 epochs) 61.89

Quick test

pretrained weights

Please download pretrained weights 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 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.
Imp. Backbone Input Size Batch Size Inference Time FPS
Paper Darknet53 320 1 22ms 45
Paper Darknet53 416 1 29ms 34
Paper Darknet53 608 1 51ms 19
Our Darknet53 416 1 28ms 36
Our Darknet53 416 8 17ms 58

Credit

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

Reference