This directory contains software developed by Ultralytics LLC. For more information on Ultralytics projects please visit: http://www.ultralytics.com
Python 3.6 or later with the following
pip3 install -U -r requirements.txt packages:
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)
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.
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).