Introduction

This directory contains software developed by Ultralytics LLC. For more information on Ultralytics projects please visit: http://www.ultralytics.com  

Description

The https://github.com/ultralytics/yolov3 repo contains code inference and training of YOLOv3 on the COCO dataset: https://cocodataset.org/#home. Credit to Joseph Redmon for YOLO (https://pjreddie.com/darknet/yolo/) and to Erik Lindernoren for the pytorch implementation this work is based on (https://github.com/eriklindernoren/PyTorch-YOLOv3).

Requirements

Python 3.6 or later with the following pip3 install -U -r requirements.txt packages:

• numpy
• torch
• opencv-python

Training

Run train.py to begin training after downloading COCO data with data/get_coco_dataset.sh. Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. An Nvidia GTX 1080 Ti will process ~10 epochs/day with full augmentation, or ~15 epochs/day without input image augmentation. Loss plots for the bounding boxes, objectness and class confidence should appear similar to results shown here (coming soon)

Inference

Checkpoints will be saved in /checkpoints directory. Run detect.py to apply trained weights to an image, such as zidane.jpg from the data/samples folder, shown here.

Testing

Run test.py to test the latest checkpoint on the 5000 validation images. Joseph Redmon's official YOLOv3 weights produce a mAP of .581 using this PyTorch implementation, compared to .579 in darknet (https://arxiv.org/abs/1804.02767).

Contact

For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at http://www.ultralytics.com/contact