YOLO-Summary

YOLO源码：

• https://github.com/pjreddie/darknet
• https://github.com/AlexeyAB/darknet 非常推荐AlexeyAB的darknet改进版
  论文：
• yolov3
• https://pjreddie.com/media/files/papers/YOLOv3.pdf
• yolov4
• https://arxiv.org/abs/2004.10934
• yolov5
• https://github.com/ultralytics/yolov5

目标检测发展进程：

• https://github.com/hoya012/deep_learning_object_detection

YOLOv3复现代码合集涵盖 5 种常用深度学习框架：

• TensorFlow
• PyTorch
• Keras
• Caffe
• MXNet

TensorFlow

Project	Infernece	Train	star
tensorflow-yolov3	√	√	1837
yolov3-tf2	√	√	795
tensorflow-yolo-v3	√	x	666
YOLOv3-tensorflow	√	√	272

PyTorch

Project	Infernece	Train	star
PyTorch-YOLOv3	√	√	2955
yolov3	√	√	2686
pytorch-yolo-v3	√	x	2291
YOLO_v3_tutorial_from_scratch	√	x	1489
ObjectDetection-OneStageDet	√	√	1471
YOLOv3_PyTorch	√	√	442
PyTorch_YOLOv3	√	√	258

Keras

Project	Infernece	Train	Star
keras-yolo3	√	√	4680
YOLOv3	√	x	505
keras-YOLOv3-mobilenet	√	√	410

Caffe

Project	Infernece	Train	Star
MobileNet-YOLO	√	√	569
caffe-yolov3	√	x	273
Caffe-YOLOv3-Windows	√	√	163

MXNet

Project	Infernece	Train	Star
gluoncv	√	√	3187

参考：

• https://zhuanlan.zhihu.com/p/50170492
• https://github.com/amusi/YOLO-Reproduce-Summary/blob/master/README.md

一、yolo框架的解读：

• https://zhuanlan.zhihu.com/p/32525231
• yolov4解读
• https://zhuanlan.zhihu.com/p/138510087

二、500问里目标检测解决的问题和yolo解读

• 第八章 目标检测
• https://github.com/scutan90/DeepLearning-500-questions

三、基于YOLO的项目

3.1使用YOLOv3训练、使用Mask-RCNN训练、理解ResNet、模型部署、人脸识别、文本分类等：

• https://github.com/StevenLei2017/AI_projects

3.2基于yolo3 与crnn 实现中文自然场景文字检测及识别

• https://github.com/chineseocr/chineseocr

3.3 YOLOv3 in PyTorch > ONNX > CoreML > iOS

• https://github.com/ultralytics/yolov3

3.4YoloV3/tiny-YoloV3+RaspberryPi3/Ubuntu LaptopPC+NCS/NCS2+USB Camera+Python+OpenVINO

• https://github.com/PINTO0309/OpenVINO-YoloV3

3.5基于darknet框架实现CTPN版本自然场景文字检测与CNN+CTCOCR文字识别

• https://github.com/chineseocr/darknet-ocr

3.6教程：用YOLO+Tesseract实现定制OCR系统

《Tutorial : Building a custom OCR using YOLO and Tesseract》

• https://medium.com/saarthi-ai/how-to-build-your-own-ocr-a5bb91b622ba

3.7基于YOLOv3的交通信号违章检测系统

Traffic Signal Violation Detection System using Computer Vision - A Computer Vision based Traffic Signal Violation Detection System from video footage using YOLOv3 & Tkinter. (GUI Included)

• https://github.com/anmspro/Traffic-Signal-Violation-Detection-System

3.8 OpenCV 'dnn' with NVIDIA GPUs: 1549% faster YOLO, SSD, and Mask R-CNN

• https://www.pyimagesearch.com/2020/02/10/opencv-dnn-with-nvidia-gpus-1549-faster-yolo-ssd-and-mask-r-cnn/

3.9 Object Detection and Tracking

• https://github.com/yehengchen/Object-Detection-and-Tracking

3.10 基于yolov3轻量级人脸检测

加入关键点的darknet训练框架，使用yolov3实现了轻量级的人脸检测

• https://github.com/ouyanghuiyu/darknet_face_with_landmark

3.11 基于D/CIoU_YOLO_V3口罩识别

• https://github.com/DataXujing/DIoU_YOLO_V3

3.12 Object Detection: YOLO, MobileNetv3 and EfficientDet

• https://github.com/imadelh/Object-Detection_MobileNetv3-EfficientDet-YOLO

3.13 Yolo-Fastest：超超超快的开源ARM实时目标检测算法

Network	VOC mAP(0.5)	COCO mAP(0.5)	Resolution	Run Time(Ncnn 1xCore)	Run Time(Ncnn 4xCore)	FLOPS	Weight size
MobileNetV2-YOLOv3-Nano	65.27	30.13	320	11.36ms	5.48ms	0.55BFlops	3.0MB
Yolo-Fastest(our)	61.02	23.65	320	6.74ms	4.42ms	0.23BFlops	1.3MB
Yolo-Fastest-XL(our)	69.43	32.45	320	15.15ms	7.09ms	0.70BFlops	3.5MB

• https://zhuanlan.zhihu.com/p/234506503
• https://github.com/dog-qiuqiu/Yolo-Fastest
• https://github.com/dog-qiuqiu/MobileNet-Yolo

3.15 OpenCV ‘dnn’ with NVIDIA GPUs: 1549% faster YOLO, SSD, and Mask R-CNN

• https://www.pyimagesearch.com/2020/02/03/how-to-use-opencvs-dnn-module-with-nvidia-gpus-cuda-and-cudnn/
• https://www.pyimagesearch.com/2020/02/10/opencv-dnn-with-nvidia-gpus-1549-faster-yolo-ssd-and-mask-r-cnn/

3.16 yolov5-face

在yolov5的基础上增加landmark预测分支，loss使用wingloss,使用yolov5s取得了相对于retinaface-r50更好的性能

• https://github.com/deepcam-cn/yolov5-face

3.17 yolov7-face

• https://github.com/jinfagang/yolov7-face

3.18 YOLO ALL YOU NEED

• https://github.com/jizhishutong/YOLOU
• https://github.com/dog-qiuqiu/FastestDet
• https://github.com/RangiLyu/nanodet
• https://github.com/iscyy/yoloair
• https://github.com/open-mmlab/mmyolo
• https://github.com/PaddlePaddle/PaddleYOLO
• https://github.com/iloveai8086/YOLOC

四、YOLO模型压缩：

4.1、剪枝：

• https://github.com/zbyuan/pruning_yolov3
• https://github.com/coldlarry/YOLOv3-complete-pruning
• https://github.com/Lam1360/YOLOv3-model-pruning
• https://github.com/tanluren/yolov3-channel-and-layer-pruning
• https://github.com/zbyuan/pruning_yolov3
• https://github.com/SpursLipu/YOLOv3-ModelCompression-MultidatasetTraining-Multibackbone

五、YOLO系列

5.1 Enriching Variety of Layer-wise Learning Information by Gradient Combination

Model	Size	mAP@0.5	BFLOPs
EfficientNet_b0-PRN	416x416	45.5	3.730
EfficientNet_b0-PRN	320x320	41.0	2.208

• https://github.com/WongKinYiu/PartialResidualNetworks

5.2 Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving

• https://github.com/jwchoi384/Gaussian_YOLOv3

5.3 YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection

Model	model Size	mAP(voc 2007)	computational cost(ops)
Tiny YOLOv2[13]	60.5MB	57.1%	6.97B
Tiny YOLOv3[14]	33.4MB	58.4%	5.52B
YOLO Nano	4.0MB	69.1%	4.57B

• https://arxiv.org/pdf/1910.01271.pdf
• https://github.com/liux0614/yolo_nano

5.4YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers

DataSet	mAP	FPS
PASCAL VOC	33.57	21
COCO	12.26	21

• https://arxiv.org/abs/1811.05588v1
• https://github.com/reu2018dl/yolo-lite
• https://mp.weixin.qq.com/s/xNaXPwI1mQsJ2Y7TT07u3g

5.5 SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications

• https://arxiv.org/ftp/arxiv/papers/1907/1907.11093.pdf
• https://github.com/PengyiZhang/SlimYOLOv3
• https://mp.weixin.qq.com/s/fDOskKqG-fsJmhT0-tdtTg

5.6 Strongeryolo-pytorch - Pytorch implementation of Stronger-Yolo with channel-pruning

Performance on VOC2007 Test(mAP) after pruning

Model	Backbone	MAP	Flops(G)	Params(M)
strongerv3	Mobilev2	79.6	4.33	6.775
strongerv3-sparsed	Mobilev2	77.4	4.33	6.775
strongerv3-Pruned(30% pruned)	Mobilev2	77.1	3.14	3.36
strongerv2	Darknet53	80.2	49.8	61.6
strongerv2-sparsed	Darknet53	78.1	49.8	61.6
strongerv2-Pruned(20% pruned)	Darknet53	76.8	49.8	45.2

• https://github.com/wlguan/Stronger-yolo-pytorch

5.7 Learning Spatial Fusion for Single-Shot Object Detection

YOLOv3+ASFF(自适应空间特征融合)组合，性能优于CornerNet和CenterNet等，在COCO上，38.1mAP/60 FPS，43.9mAP/29FPS！

System	test-dev mAP	Time (V100)	Time (2080ti)
YOLOv3 608	33.0	20ms	24ms
YOLOv3 608+ BoFs	37.0	20ms	24ms
YOLOv3 608(ours baseline)	38.8	20ms	24ms
YOLOv3 608+ ASFF	40.6	22ms	28ms
YOLOv3 608+ ASFF*	42.4	22ms	29ms
YOLOv3 800+ ASFF*	43.9	34ms	40ms

• https://arxiv.org/pdf/1911.09516.pdf
• https://github.com/ruinmessi/ASFF
• https://zhuanlan.zhihu.com/p/110205719

5.8 Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

• https://arxiv.org/pdf/1911.08287.pdf
• https://github.com/Zzh-tju/DIoU-darknet
• https://mp.weixin.qq.com/s/St5WevfcVt4RubJsY-ZEHw

5.9 xYOLO: A Model For Real-Time Object Detection In Humanoid Soccer On Low-End Hardware

• https://arxiv.org/abs/1910.03159

5.10、CSPNet: A New Backbone that can Enhance Learning Capability of CNN

• https://arxiv.org/abs/1911.11929
• https://github.com/WongKinYiu/CrossStagePartialNetworks

5.11、Spiking-YOLO: Spiking Neural Network for Real-time Object Detection

• https://arxiv.org/pdf/1903.06530.pdf

5.12、 Enriching Variety of Layer-wise Learning Information by Gradient Combination

• https://github.com/WongKinYiu/PartialResidualNetworks/blob/master/pdf/iccvw-prn.pdf
• https://github.com/WongKinYiu/PartialResidualNetworks

5.13、YOLOv4: Optimal Speed and Accuracy of Object Detection

• https://arxiv.org/abs/2004.10934
• https://github.com/AlexeyAB/darknet

5.14、PP-YOLO: An Effective and Efficient Implementation of Object Detector

• https://arxiv.org/abs/2007.12099
• https://github.com/PaddlePaddle/PaddleDetection
• https://zhuanlan.zhihu.com/p/163580627

5.15、Scaled-YOLOv4: Scaling Cross Stage Partial Network

• https://arxiv.org/abs/2011.08036
• https://github.com/WongKinYiu/ScaledYOLOv4

5.16、You Only Look One-level Feature

• https://arxiv.org/abs/2103.09460
• https://github.com/megvii-model/YOLOF

5.17、You Only Learn One Representation: Unified Network for Multiple Tasks

• https://arxiv.org/abs/2105.04206
• https://github.com/WongKinYiu/yolor

5.18、YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

• https://arxiv.org/abs/2207.02696
• https://github.com/WongKinYiu/yolov7

5.19、YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

• https://arxiv.org/abs/2209.02976
• https://github.com/meituan/YOLOv6

5.20、YOLOX: Exceeding YOLO Series in 2021

• https://arxiv.org/abs/2107.08430
• https://github.com/Megvii-BaseDetection/YOLOX

5.21 You Only 👀 Once for Panoptic ​ 🚗 Perception

• https://arxiv.org/abs/2108.11250
• https://github.com/hustvl/YOLOP

5.22 YOLOPv2:rocket:: Better, Faster, Stronger for Panoptic driving Perception

• https://arxiv.org/abs/2208.11434
• https://github.com/CAIC-AD/YOLOPv2