Explained Practically how to use yolov5 on Custom dataset.

Github link: https://github.com/AarohiSingla/yolov5

Dataset Used: : https://www.kaggle.com/valentynsichka...

YOLOv5 is the latest version of YOLO family of object detection models. It's the first YOLO implementation in PyTorch (rather than Darknet) and emphasizes ease of use and quickness of training and inference. This YOLOv5 tutorial shows you how to train the model on your own dataset in Python and how to test your Model.

What is YOLO?
YOLO stands for You Only Look Once
YOLO is an algorithm that uses neural networks to provide realtime object detection. This algorithm is popular because of its speed and accuracy. It has been used in various applications to detect traffic signals, people, parking meters, and animals.
With the timeline, it has become faster and better, with its versions named as:
YOLO V1
YOLO V2
YOLO V3
YOLO V4
YOLO V5
YOLO V2 is better than V1 in terms of accuracy and speed.
YOLO V3 is not faster than V2 but is more accurate than V2 and so on.

How the YOLO algorithm works?
YOLO algorithm works using the following three techniques:

1 Residual blocks: image is divided into various grids. Each grid has a dimension of n X n
2 Bounding box regression
3 Intersection Over Union (IOU) : YOLO uses IOU to provide an output box that surrounds the objects perfectly.

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