【YOLO】YOLOv5

论文地址：https://arxiv.org/pdf/1506.02640.pdf
代码地址(yolov5)：https://github.com/ultralytics/yolov5
入门说明：https://zhuanlan.zhihu.com/p/25236464
深入浅出Yolo系列之Yolov5核心基础知识完整讲解: https://zhuanlan.zhihu.com/p/172121380

region proposal 概念？？？ https://zhuanlan.zhihu.com/p/338217417

发展历程

B站学习网站：https://www.bilibili.com/video/BV1sF411F7yS?p=2&vd_source=722db5440bd2b088e4f9197f23ba286e

方向名称：Object Detection（目标检测/物体检测）
定位（Where?）：Localization,识别（What?）:Recogntion
类别标签：Catagory Label,置信度得分：Confidence score
矩形框：Bounding box

其他任务：单个物体：Classification,Classification+Localization,
多个物体：Object Detection ,Ins.. Segmentation

定位和检测：
定位是找到检测图像中带有一个给定标签的单个目标
检测是找到图像中带有给定标签的所有目标

目标检测领域发论文常用数据集：PASCAL VOC ,MS COCO

PASCAL VOC 2007:9963张图像，24640个标注;PASCAL VOC 2012:11530张图像,27450个标注该数据集有20个分类:
链接: http://host.robots.ox.ac.uk/pascal/voc/voc2012/

coco (Common Objects in Context）数据集包含20万个图像:11.5万多张训练集图像，5千张验证集图像，2万多张测试集图像
80个类别中有超过50万个目标标注。平均每个图像的目标数为7.2
MS COCO 链接：http: //cocodataset.org/

数据集代码中文件夹规范：

下采样:\(2^5=32\)

Yolov5模型结构&损失函数
YOLOv5网络结构：

Loss函数的计算，改进Loss的介绍：

Yolov5相关评价指标
目标检测性能指标

检测精度
·Precision, Recall, F1 score
·lou (Intersection over Union)
·P-R curve (Precison-Recall curve)
·AP (Average Precision)
.mAP (mean Average Precision)

检测速度
·前传耗时
·每秒帧数FPS(Frames Per Second)
·浮点运算量(FLOPS)

F1值的计算：

判断模型框的准不准，指标IOU：

An loU of 1 implies that predicted and the ground-truth bounding boxes perfectly overlap.
You can set a threshold value for the loU to determine if the object detection is valid or not.Let's say you set lou to 0.5, in that case
. if lou ≥0.5, classify the object detection as True Positive(TP)
. if loU <0.5, then it is a wrong detection and classify it as False Positive(FP)
·When a ground truth is present in the image and model failed to detect the object, classifyit as False Negative(FN).
·True Negative (TN):TN is every part of the image where we did not predict an object.Thismetrics is not useful for object detection, hence we ignore TN.

·AP衡量的是学习出来的模型在每个类别上的好坏
.mAP衡量的是学出的模型在所有类别上的好坏。mAP就是取所有类别上AP的平均值