The practical guide for Object Detection with YOLOv5 algorithm

The practical guide for Object Detection with YOLOv5 algorithm

The practical guide for Object Detection with YOLOv5 algorithm

“Identification of objects in an image considered a common assignment for the human brain, though not so trivial for a machine. Identification and localization of objects in photos is a computer vision task called ‘object detection’, and several algorithms has emerged in the past few years to tackle the problem. One of the most popular algorithms to date for real-time object detection is YOLO (You Only Look Once), initially proposed by Redmond et. al [1].

In this tutorial you will learn to perform an end-to-end object detection project on a custom dataset, using the latest YOLOv5 implementation developed by Ultralytics [2]. We will use transfer-learning techniques to train our own model, evaluate its performances, use it for inference and even convert it to other file formats such as ONNX and TensorRT.

The tutorial is oriented to people with theoretical background of object detection algorithms, who seek for a practical implementation guidance. An easy-to-use Jupiter notebook with the full code is provided below for your convenience…”

Source: towardsdatascience.com/the-practical-guide-for-object-detection-with-yolov5-algorithm-74c04aac4843