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yolov3 in PyTorch

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https://github.com/ultralytics/yolov3

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.

AugmentationDescription
Translation+/- 10% (vertical and horizontal)
Rotation+/- 5 degrees
Shear+/- 2 degrees (vertical and horizontal)
Scale+/- 10%
Reflection50% 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

GPUn--batch --accumimg/sepoch
time		epoch
cost
K80132 x 211175 min$0.58
T41
2		32 x 2
64 x 1		41
61		48 min
32 min		$0.28
$0.36
V1001
2		32 x 2
64 x 1		122
178		16 min
11 min		$0.23
$0.31
2080Ti1
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
 SizeCOCO mAP
@0.5...0.95		COCO mAP
@0.5
YOLOv3-tiny
YOLOv3
YOLOv3-SPP
YOLOv3-SPP ultralytics		32014.0
28.7
30.5
35.4		29.1
51.8
52.3
54.3
YOLOv3-tiny
YOLOv3
YOLOv3-SPP
YOLOv3-SPP ultralytics		41616.0
31.2
33.9
39.0		33.0
55.4
56.9
59.2
YOLOv3-tiny
YOLOv3
YOLOv3-SPP
YOLOv3-SPP ultralytics		51216.6
32.7
35.6
40.3		34.9
57.7
59.5
60.6
YOLOv3-tiny
YOLOv3
YOLOv3-SPP
YOLOv3-SPP ultralytics		60816.6
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.ptNamespace(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.