# Introduction This directory contains software developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information on Ultralytics projects please visit: https://www.ultralytics.com. # Description The https://github.com/ultralytics/yolov3 repo contains inference and training code for YOLOv3 in PyTorch. The code works on Linux, MacOS and Windows. 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 3.7 or later with the following `pip3 install -U -r requirements.txt` packages: - `numpy` - `torch >= 1.0.0` - `opencv-python` # Training **Start Training:** Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. Training runs about 1 hour per COCO epoch on a 1080 Ti. **Resume Training:** Run `train.py --resume` to resume training from the most recently saved checkpoint `weights/latest.pt`. Each epoch trains on 120,000 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set. Default training settings produce loss plots below, with **training speed of 0.6 s/batch on a 1080 Ti (15 epochs/day)** or 0.45 s/batch on a 2080 Ti.  ## Image Augmentation `datasets.py` applies random OpenCV-powered (https://opencv.org/) augmentation to the input images in accordance with the following specifications. Augmentation is applied **only** during training, not during inference. Bounding boxes are automatically tracked and updated with the images. 416 x 416 examples pictured below. Augmentation | Description --- | --- Translation | +/- 10% (vertical and horizontal) Rotation | +/- 5 degrees Shear | +/- 2 degrees (vertical and horizontal) Scale | +/- 10% Reflection | 50% probability (horizontal-only) H**S**V Saturation | +/- 50% HS**V** Intensity | +/- 50% # Inference Run `detect.py` to apply trained weights to an image and visualize results, such as `zidane.jpg` from the `data/samples` folder, shown here. **YOLOv3:** `detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.pt` **YOLOv3-tiny:** `detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.pt` # Pretrained Weights Download official YOLOv3 weights: **Darknet** format: - https://pjreddie.com/media/files/yolov3.weights - https://pjreddie.com/media/files/yolov3-tiny.weights **PyTorch** format: - https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI # Validation mAP Run `test.py` to validate the official YOLOv3 weights `weights/yolov3.weights` against the 5000 validation images. You should obtain a .584 mAP at `--img-size 416`, or .586 at `--img-size 608` using this repo, compared to .579 at 608 x 608 reported in darknet (https://arxiv.org/abs/1804.02767). Run `test.py --weights weights/latest.pt` to validate against the latest training results. **Default training settings produce a 0.522 mAP at epoch 62.** Hyperparameter settings and loss equation changes affect these results significantly, and additional trade studies may be needed to further improve this. # Contact For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com.