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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 inference and training code for YOLOv3 in PyTorch. Training is done on the COCO dataset by default: 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:
numpytorchopencv-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 image augmentation, or ~15 epochs/day with no augmentation. Loss plots for bounding boxes, objectness and classification should appear similar to results shown here (training currently in-progress to 160 epochs).
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