v2.2	v3.0

# Introduction This directory contains python software and an iOS App developed by Ultralytics LLC, and **is freely available for redistribution under the GPL-3.0 license**. For more information 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` # Tutorials * [Transfer Learning](https://github.com/ultralytics/yolov3/wiki/Example:-Transfer-Learning) * [Train Single Image](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Image) * [Train Single Class](https://github.com/ultralytics/yolov3/wiki/Example:-Train-Single-Class) * [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) # Training **Start Training:** Run `train.py` to begin training after downloading COCO data with `data/get_coco_dataset.sh`. **Resume Training:** Run `train.py --resume` resumes training from the latest 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 (18 epochs/day)** or 0.45 s/batch on a 2080 Ti. `from utils import utils; utils.plot_results()`  ## 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% ## Speed https://cloud.google.com/deep-learning-vm/ **Machine type:** n1-highmem-4 (4 vCPUs, 26 GB memory) **CPU platform:** Intel Skylake **GPUs:** 1-4 x NVIDIA Tesla P100 **HDD:** 100 GB SSD GPUs | `batch_size` | speed | COCO epoch --- |---| --- | --- (P100) | (images) | (s/batch) | (min/epoch) 1 | 16 | 0.54s | 66min 2 | 32 | 0.99s | 61min 4 | 64 | 1.61s | 49min # Inference Run `detect.py` to apply trained weights to an image, such as `zidane.jpg` from the `data/samples` folder: **YOLOv3:** `python3 detect.py --cfg cfg/yolov3.cfg --weights weights/yolov3.pt` **YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.pt` **YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights weights/yolov3-spp.pt` ## Webcam Run `detect.py` with `webcam=True` to show a live webcam feed. # Pretrained Weights - Darknet `*.weights` format: https://pjreddie.com/media/files/yolov3.weights - PyTorch `*.pt` format: https://drive.google.com/drive/folders/1uxgUBemJVw9wZsdpboYbzUN4bcRhsuAI # mAP - Use `test.py --weights weights/yolov3.weights` to test the official YOLOv3 weights. - Use `test.py --weights weights/latest.pt` to test the latest training results. - Compare to official darknet results from https://arxiv.org/abs/1804.02767. | ultralytics/yolov3 | darknet --- | ---| --- YOLOv3-320 | 51.3 | 51.5 YOLOv3-416 | 54.9 | 55.3 YOLOv3-608 | 57.9 | 57.9 ``` bash sudo rm -rf yolov3 && git clone https://github.com/ultralytics/yolov3 # bash yolov3/data/get_coco_dataset.sh sudo rm -rf cocoapi && git clone https://github.com/cocodataset/cocoapi && cd cocoapi/PythonAPI && make && cd ../.. && cp -r cocoapi/PythonAPI/pycocotools yolov3 cd yolov3 python3 test.py --save-json --conf-thres 0.001 --img-size 416 Namespace(batch_size=32, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=416, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights') Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.308 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.549 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.310 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.141 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.334 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.454 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.267 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.403 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.237 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.464 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.585 python3 test.py --save-json --conf-thres 0.001 --img-size 608 --batch-size 16 Namespace(batch_size=16, cfg='cfg/yolov3.cfg', conf_thres=0.001, data_cfg='cfg/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.45, save_json=True, weights='weights/yolov3.weights') Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.328 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.579 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.335 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.190 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.357 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.428 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.279 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.429 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.456 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.299 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.572 ``` # Contact For questions or comments please contact Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com.