V2AS问路
yolov3 in PyTorch
阅读原文时间：2023年07月15日阅读：3

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

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.

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%

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

$ 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:

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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