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/. # Requirements Python 3.7 or later with the following `pip3 install -U -r requirements.txt` packages: - `numpy` - `torch >= 1.0.0` - `opencv-python` - `tqdm` # Tutorials * [GCP Quickstart](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) * [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-standard-8 (8 vCPUs, 30 GB memory) **CPU platform:** Intel Skylake **GPUs:** K80 ($0.198/hr), P4 ($0.279/hr), T4 ($0.353/hr), P100 ($0.493/hr), V100 ($0.803/hr) **HDD:** 100 GB SSD **Dataset:** COCO train 2014 GPUs | `batch_size` | batch time | epoch time | epoch cost --- |---| --- | --- | --- | (images) | (s/batch) | | 1 K80 | 16 | 1.43s | 175min | $0.58 1 P4 | 8 | 0.51s | 125min | $0.58 1 T4 | 16 | 0.78s | 94min | $0.55 1 P100 | 16 | 0.39s | 48min | $0.39 2 P100 | 32 | 0.48s | 29min | $0.47 4 P100 | 64 | 0.65s | 20min | $0.65 1 V100 | 16 | 0.25s | 31min | $0.41 2 V100 | 32 | 0.29s | 18min | $0.48 4 V100 | 64 | 0.41s | 13min | $0.70 8 V100 | 128 | 0.49s | 7min | $0.80 # 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.weights` **YOLOv3-tiny:** `python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights weights/yolov3-tiny.weights` **YOLOv3-SPP:** `python3 detect.py --cfg cfg/yolov3-spp.cfg --weights weights/yolov3-spp.weights` ## 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 darknet published results https://arxiv.org/abs/1804.02767. | [ultralytics/yolov3](https://github.com/ultralytics/yolov3) | [darknet](https://arxiv.org/abs/1804.02767) --- | --- | --- `YOLOv3 320` | 51.8 | 51.5 `YOLOv3 416` | 55.4 | 55.3 `YOLOv3 608` | 58.2 | 57.9 `YOLOv3-spp 320` | 52.4 | - `YOLOv3-spp 416` | 56.5 | - `YOLOv3-spp 608` | 60.7 | 60.6 ``` 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 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=0.5, save_json=True, weights='weights/yolov3.weights') Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80) Image Total P R mAP Calculating mAP: 100%|█████████████████████████████████| 313/313 [08:54<00:00, 1.55s/it] 5000 5000 0.0966 0.786 0.579 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.331 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.582 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.344 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.198 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.362 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.427 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.281 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.437 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.463 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.309 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.494 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.577 python3 test.py --weights weights/yolov3-spp.weights --cfg cfg/yolov3-spp.cfg --save-json --img-size 608 --batch-size 8 Namespace(batch_size=8, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data_cfg='data/coco.data', img_size=608, iou_thres=0.5, nms_thres=0.5, save_json=True, weights='weights/yolov3-spp.weights') Using cuda _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80) Image Total P R mAP Calculating mAP: 100%|█████████████████████████████████| 625/625 [07:01<00:00, 1.56it/s] 5000 5000 0.12 0.81 0.611 Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.366 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.607 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.386 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.207 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.391 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.485 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.296 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.464 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.494 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.331 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.517 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.618 ``` # Citation [](https://zenodo.org/badge/latestdoi/146165888) # Contact Issues should be raised directly in the repository. For additional questions or comments please email Glenn Jocher at glenn.jocher@ultralytics.com or visit us at https://contact.ultralytics.com/contact.