Introduction简介

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.

此目录包含由Ultralytics LLC开发的PyTorch YOLOv3软件，可根据GPL-3.0许可证免费重新分发。更多信息请访问https://www.ultralytics.com。

Description描述

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/.

https://github.com/ultralytics/yolov3 托管包含PyTorch中yolov3的引用和训练代码。这段代码可以在Linux、MacOS和Windows上运行。默认情况下，训练是在COCO数据集上完成的：https://COCO dataset.org/'35；home。YOLO的版权归属于约瑟夫·雷蒙：https://pjreddie.com/darknet/YOLO/。

Requirements

Python 3.7 or later with the following pip3 install -U -r requirements.txt packages:

numpy
torch >= 1.1.0
opencv-python
tqdm

Tutorials教程

GCP Quickstart快速开始GCP
Transfer Learning迁移学习
Train Single Image训练单张图片
Train Single Class训练单个类别
Train Custom Data训练自己的数据

Jupyter Notebook

Our Jupyter notebook provides quick training, inference and testing examples.

我们的Jupyter笔记本提供了快速的培训、推理和测试示例。

Training

Start Training: python3 train.py to begin training after downloading COCO data with data/get_coco_dataset.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.

在使用data/get_coco_dataset.sh下載了COCO數據集之後使用python3 train.py開始訓練。

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

Plot Training: from utils import utils; utils.plot_results() plots training results from coco_16img.data, coco_64img.data, 2 example datasets available in the data/ folder, which train and test on the first 16 and 64 images of the COCO2014-trainval dataset.

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)
HSV Saturation	+/- 50%
HSV Intensity	+/- 50%

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

detect.py runs inference on any sources:

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

To run a specific models:

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

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

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

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

python3 test.py --weights ... --cfg ...

mAP@0.5 run at --nms-thres 0.5, mAP@0.5...0.95 run at --nms-thres 0.7
YOLOv3-SPP ultralytics is ultralytics68.pt with yolov3-spp.cfg
Darknet results: https://arxiv.org/abs/1804.02767

	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 35.4	29.1 51.8 52.3 54.3
YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP ultralytics	416	16.0 31.2 33.9 39.0	33.0 55.4 56.9 59.2
YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP ultralytics	512	16.6 32.7 35.6 40.3	34.9 57.7 59.5 60.6
YOLOv3-tiny YOLOv3 YOLOv3-SPP YOLOv3-SPP ultralytics	608	16.6 33.1 37.0 40.9	35.4 58.2 60.7 60.9

$ python3 test.py --save-json --img-size 608 --nms-thres 0.5 --weights ultralytics68.pt

Namespace(batch_size=16, cfg='cfg/yolov3-spp.cfg', conf_thres=0.001, data='data/coco.data', device='1', img_size=608, iou_thres=0.5, nms_thres=0.7, save_json=True, weights='ultralytics68.pt')

Using CUDA device0 _CudaDeviceProperties(name='GeForce RTX 2080 Ti', total_memory=11019MB)

               Class    Images   Targets         P         R   mAP@0.5        F1: 100%|███████████████████████████████████████████████████████████████████████████████████| 313/313 [09:46<00:00,  1.09it/s]

                 all     5e+03  3.58e+04    0.0823     0.798     0.595     0.145

              person     5e+03  1.09e+04    0.0999     0.903     0.771      0.18

             bicycle     5e+03       316    0.0491     0.782      0.56    0.0925

                 car     5e+03  1.67e+03    0.0552     0.845     0.646     0.104

          motorcycle     5e+03       391      0.11     0.847     0.704     0.194

            airplane     5e+03       131     0.099     0.947     0.878     0.179

                 bus     5e+03       261     0.142     0.874     0.825     0.244

               train     5e+03       212     0.152     0.863     0.806     0.258

               truck     5e+03       352    0.0849     0.682     0.514     0.151

                boat     5e+03       475    0.0498     0.787     0.504    0.0937

       traffic light     5e+03       516    0.0304     0.752     0.516    0.0584

        fire hydrant     5e+03        83     0.144     0.916     0.882     0.248

           stop sign     5e+03        84    0.0833     0.917     0.809     0.153

       parking meter     5e+03        59    0.0607     0.695     0.611     0.112

               bench     5e+03       473    0.0294     0.685     0.363    0.0564

                bird     5e+03       469    0.0521     0.716     0.524    0.0972

                 cat     5e+03       195     0.252     0.908      0.78     0.395

                 dog     5e+03       223     0.192     0.883     0.829     0.315

               horse     5e+03       305     0.121     0.911     0.843     0.214

               sheep     5e+03       321     0.114     0.854     0.724     0.201

                 cow     5e+03       384     0.105     0.849     0.695     0.187

            elephant     5e+03       284     0.184     0.944     0.912     0.308

                bear     5e+03        53     0.358     0.925     0.875     0.516

               zebra     5e+03       277     0.176     0.935     0.858     0.297

             giraffe     5e+03       170     0.171     0.959     0.892      0.29

            backpack     5e+03       384    0.0426     0.708     0.392    0.0803

            umbrella     5e+03       392    0.0672     0.878      0.65     0.125

             handbag     5e+03       483    0.0238     0.629     0.242    0.0458

                 tie     5e+03       297    0.0419     0.805     0.599    0.0797

            suitcase     5e+03       310    0.0823     0.855     0.628      0.15

             frisbee     5e+03       109     0.126     0.872     0.796     0.221

                skis     5e+03       282    0.0473     0.748     0.454     0.089

           snowboard     5e+03        92    0.0579     0.804     0.559     0.108

         sports ball     5e+03       236     0.057     0.733     0.622     0.106

                kite     5e+03       399     0.087     0.852     0.645     0.158

        baseball bat     5e+03       125    0.0496     0.776     0.603    0.0932

      baseball glove     5e+03       139    0.0511     0.734     0.563    0.0956

          skateboard     5e+03       218    0.0655     0.844      0.73     0.122

           surfboard     5e+03       266    0.0709     0.827     0.651     0.131

       tennis racket     5e+03       183    0.0694     0.858     0.759     0.128

              bottle     5e+03       966    0.0484     0.812     0.513    0.0914

          wine glass     5e+03       366    0.0735     0.738     0.543     0.134

                 cup     5e+03       897    0.0637     0.788     0.538     0.118

                fork     5e+03       234    0.0411     0.662     0.487    0.0774

               knife     5e+03       291    0.0334     0.557     0.292    0.0631

               spoon     5e+03       253    0.0281     0.621     0.307    0.0537

                bowl     5e+03       620    0.0624     0.795     0.514     0.116

              banana     5e+03       371     0.052      0.83      0.41    0.0979

               apple     5e+03       158    0.0293     0.741     0.262    0.0564

            sandwich     5e+03       160    0.0913     0.725     0.522     0.162

              orange     5e+03       189    0.0382     0.688      0.32    0.0723

            broccoli     5e+03       332    0.0513      0.88     0.445     0.097

              carrot     5e+03       346    0.0398     0.766     0.362    0.0757

             hot dog     5e+03       164    0.0958     0.646     0.494     0.167

               pizza     5e+03       224    0.0886     0.875     0.699     0.161

               donut     5e+03       237    0.0925     0.827      0.64     0.166

                cake     5e+03       241    0.0658      0.71     0.539      0.12

               chair     5e+03  1.62e+03    0.0432     0.793     0.489    0.0819

               couch     5e+03       236     0.118     0.801     0.584     0.205

        potted plant     5e+03       431    0.0373     0.852     0.505    0.0714

                 bed     5e+03       195     0.149     0.846     0.693     0.253

        dining table     5e+03       634    0.0546      0.82      0.49     0.102

              toilet     5e+03       179     0.161      0.95      0.81     0.275

                  tv     5e+03       257    0.0922     0.903      0.79     0.167

              laptop     5e+03       237     0.127     0.869     0.744     0.222

               mouse     5e+03        95    0.0648     0.863     0.732      0.12

              remote     5e+03       241    0.0436     0.788     0.535    0.0827

            keyboard     5e+03       117    0.0668     0.923     0.755     0.125

          cell phone     5e+03       291    0.0364     0.704     0.436    0.0692

           microwave     5e+03        88     0.154     0.841     0.743     0.261

                oven     5e+03       142    0.0618     0.803     0.576     0.115

             toaster     5e+03        11    0.0565     0.636     0.191     0.104

                sink     5e+03       211    0.0439     0.853     0.544    0.0835

        refrigerator     5e+03       107    0.0791     0.907     0.742     0.145

                book     5e+03  1.08e+03    0.0399     0.667     0.233    0.0753

               clock     5e+03       292    0.0542     0.836     0.733     0.102

                vase     5e+03       353    0.0675     0.799     0.591     0.125

            scissors     5e+03        56    0.0397      0.75     0.461    0.0755

          teddy bear     5e+03       245    0.0995     0.882     0.669     0.179

          hair drier     5e+03        11   0.00508    0.0909    0.0475   0.00962

          toothbrush     5e+03        77    0.0371      0.74     0.418    0.0706

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.409

 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.600

 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.446

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.243

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.450

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.514

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.326

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.536

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.593

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.422

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.640

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.707

Reproduce Our Results

This command trains yolov3-spp.cfg from scratch to our mAP above. Training takes about one week on a 2080Ti.

$ python3 train.py --weights '' --cfg yolov3-spp.cfg --epochs 273 --batch 16 --accum 4 --multi --pre

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
Google Colab Notebook with 12 hours of free GPU time: Google Colab Notebook
Docker Image from https://hub.docker.com/r/ultralytics/yolov3. See Docker Quickstart Guide

Citation

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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