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Introduction

This directory contains PyTorch YOLOv3 software 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 all of the pip install -U -r requirements.txt packages including:

torch >= 1.4
opencv-python
Pillow

All dependencies are included in the associated docker images. Docker requirements are:

Nvidia Driver >= 440.44
Docker Engine - CE >= 19.03

Tutorials

Train Custom Data < highly recommended!!
Train Single Class
Google Colab Notebook with quick training, inference and testing examples
GCP Quickstart
Docker Quickstart Guide
A TensorRT Implementation of YOLOv3-SPP

Training

Start Training: python3 train.py to begin training after downloading COCO data with data/get_coco2017.sh. Each epoch trains on 117,263 images from the train and validate COCO sets, and tests on 5000 images from the COCO validate set.

Resume Training: python3 train.py --resume to resume training from weights/last.pt.

Plot Training: from utils import utils; utils.plot_results()

Image Augmentation

datasets.py applies OpenCV-powered (https://opencv.org/) augmentation to the input image. We use a mosaic dataloader (pictured below) to increase image variability during training.

Speed

https://cloud.google.com/deep-learning-vm/
Machine type: preemptible n1-standard-16 (16 vCPUs, 60 GB memory)
CPU platform: Intel Skylake
GPUs: K80 ($0.20/hr), T4 ($0.35/hr), V100 ($0.83/hr) CUDA with Nvidia Apex FP16/32
HDD: 1 TB SSD
Dataset: COCO train 2014 (117,263 images)
Model: yolov3-spp.cfg
Command: python3 train.py --img 416 --batch 32 --accum 2

GPU	n	`--batch --accum`	img/s	epoch time	epoch cost
K80	1	32 x 2	11	175 min	$0.58
T4	1 2	32 x 2 64 x 1	41 61	48 min 32 min	$0.28 $0.36
V100	1 2	32 x 2 64 x 1	122 178	16 min 11 min	$0.23 $0.31
2080Ti	1 2	32 x 2 64 x 1	81 140	24 min 14 min	- -

Inference

python3 detect.py --source ...

Image: --source file.jpg
Video: --source file.mp4
Directory: --source dir/
Webcam: --source 0
RTSP stream: --source rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa
HTTP stream: --source http://wmccpinetop.axiscam.net/mjpg/video.mjpg

YOLOv3: python3 detect.py --cfg cfg/yolov3.cfg --weights yolov3.pt

YOLOv3-tiny: python3 detect.py --cfg cfg/yolov3-tiny.cfg --weights yolov3-tiny.pt

YOLOv3-SPP: python3 detect.py --cfg cfg/yolov3-spp.cfg --weights yolov3-spp.pt

Pretrained Weights

Download from: https://drive.google.com/open?id=1LezFG5g3BCW6iYaV89B2i64cqEUZD7e0

Darknet Conversion

$ git clone https://github.com/ultralytics/yolov3 && cd yolov3

# convert darknet cfg/weights to pytorch model
$ python3  -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.weights')"
Success: converted 'weights/yolov3-spp.weights' to 'converted.pt'

# convert cfg/pytorch model to darknet weights
$ python3  -c "from models import *; convert('cfg/yolov3-spp.cfg', 'weights/yolov3-spp.pt')"
Success: converted 'weights/yolov3-spp.pt' to 'converted.weights'

mAP

	Size	COCO mAP @0.5...0.95	COCO mAP @0.5
YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics	320	14.0 28.7 30.5 37.7	29.1 51.8 52.3 56.8
YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics	416	16.0 31.2 33.9 41.2	33.0 55.4 56.9 60.6
YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics	512	16.6 32.7 35.6 42.6	34.9 57.7 59.5 62.4
YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP-ultralytics	608	16.6 33.1 37.0 43.1	35.4 58.2 60.7 62.8

mAP@0.5 run at --iou-thr 0.5, mAP@0.5...0.95 run at --iou-thr 0.7
Darknet results: https://arxiv.org/abs/1804.02767

$ python3 test.py --cfg yolov3-spp.cfg --weights yolov3-spp-ultralytics.pt --img 640 --augment

Namespace(augment=True, batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='coco2014.data', device='', img_size=640, iou_thres=0.6, save_json=True, single_cls=False, task='test', weights='weight
Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)

               Class    Images   Targets         P         R   mAP@0.5        F1: 100%|█████████| 313/313 [03:00<00:00,  1.74it/s]
                 all     5e+03  3.51e+04     0.375     0.743      0.64     0.492

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.455
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.646
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.496
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.263
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.501
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.596
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.361
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.597
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.666
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.492
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.719
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.810

Speed: 17.5/2.5/20.1 ms inference/NMS/total per 640x640 image at batch-size 16

# Reproduce Our Results This command trains `yolov3-spp.cfg` from scratch to our mAP above. Training takes about one week on a 2080Ti. ```bash $ python3 train.py --weights '' --cfg yolov3-spp.cfg --epochs 300 --batch 16 --accum 4 --multi ```

# Reproduce Our Environment To access an up-to-date working environment (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled), consider a: - **GCP** Deep Learning VM with $300 free credit offer: See our [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart) - **Google Colab Notebook** with 12 hours of free GPU time: [Google Colab Notebook](https://colab.research.google.com/drive/1G8T-VFxQkjDe4idzN8F-hbIBqkkkQnxw) - **Docker Image** from https://hub.docker.com/r/ultralytics/yolov3. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) # Citation [![DOI](https://zenodo.org/badge/146165888.svg)](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.

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