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YOLOv3

Vector

Description

This repository is a product when I started learning object detection. It contains implementation of training and inference code of YOLOv3 in PyTorch. It is self-contained on mainstream platform and it supports custom data trianing and multi GPUs as well. Credit to Joseph Redmon for YOLO and the paper can be found here. I am highly inspired by these two repositories:

Followed their codes, helped me go through the yolov3 algorithm. Then I reconstructed & rewrited them and made some modifications to try to reimplement it. It is recommended to read their codes to see the specific implementation steps and logic. Also you can just go to the Dev for more details of YOLOv3.

Requirements

  • python >= 3.6
  • numpy
  • torch >= 1.0.0
  • opencv
  • json
  • matplotlib
  • tensorboardX
  • CUDA(optional)

Download Data

$ cd data/
$ bash get_coco_dataset.sh

Pretrained Weights

The pretrained weights can be found on Google Drive, download yolov3-tiny.conv.15 & darknet53.conv.74 and place under the weights folder. Actually, they can be downloaded automatically. However, if you want to train the model from scratch, you can skip this step.

Training

  • COCO dataset Just open train.ipynb and run all cells respectively. It will train yolov3 using COCO dataset. Using FROM_SCRATCH to control whether train from scratch.
  • Custom dataset For custom data training, you should get your own data ready and make annotations format is the same as yolo's. Bascially, you should modidy coco.data & coco.names to satisfy your dataset and also you should modify *.cfg file, make yolo layers outputs satisfy the number of class of your dataset. There is a nice instruction about how to train custom dataset, which can be found here. After you get everything ready, run all cells of train.ipynb.

Inference

Open detect.ipynb and run it. It can detect objects for a single image or more under sample folder. The result images with predicted bounding box are saved under output folder.

License

LICENSE

Notice

  • Please note, this is a research project! It should not be used as a definitive guide on object detection. Many engineering features have not been implemented. The demo should be considered for research and entertainment value only.
  • The used images were from web, please contact me if they infringe your digital copyright.
  • For the limatation of computing resource, I'll update the training result gradually.

Reference