CodeMrSheep / garbage-detect-using-YOLOv3-pytorch

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介绍

这个仓库包含的是基于u版(ultralytics)的Pytorch YOLOv3模型,笔者使用的是2020年4月25日更新的版本,并在原版的YOLOv3的基础上,进行了custom优化,如在YOLO的neck增加RFB模型、ECBAM模型等,同时对YOLO的FPN连接方式做了更改(原始的FPN和笔者所加的模块存在一些冲突,会降mAP),具体可以参考项目中cfg/文件夹下的yolov3-haihua-assistant-35-rfbs-61-rfbs-distance-1-to-head.cfg.

当然,笔者所做的优化一定存在可以继续优化的空间,如果你发现一些错误和新的创新点,欢迎star并联系我!

深度学习环境要求

Python 3.7 或者 你可以使用pip install -U -r requirements.txt 进行安装所有的包:

  • torch >= 1.4
  • opencv-python
  • Pillow

同样,可以使用Docker镜像,不过笔者没用过,感兴趣的小伙伴可以参考 https://github.com/ultralytics/yolov3 自己探索. Docker 的硬件需求:

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

使用方法

这里的使用方法沿用u版的使用向导,主要区别是数据处理部分的工作 笔者所使用的的数据集的连接请自行下载:

数据集链接:https://pan.baidu.com/s/1AnN2WV_AgQ0HmsxtM9OLMw 提取码:9ui8

预训练权重使用的COCO的darknet预训练权重链接:https://pan.baidu.com/s/1YDCfx1YEW4OP7dH9kKOG8g 提取码:9ui8

训练

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
$ 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 ] = 0.456
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.647
 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: 17.5/2.3/19.9 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.

$ 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:

Citation

DOI

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.

About

License:GNU General Public License v3.0


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