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

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 ] = 0.456
 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:

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.

ChengLeiWei/garbage-detect-using-YOLOv3-pytorch

介绍