Object detection using YoloV5 (Part 2)



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Object detection using YoloV5 (Part 2)

YOLOv5 is the first of the YOLO models which is written in the PyTorch framework and it’s simple and easy to use. YoloV5 is an objection detection which is already trained on a large dataset and thousands of classes. Yolov5 is basically used for object detection which is used for detecting objects in images as well as videos. We can directly train the YoloV5 model for object detection to detect the objects. We can use the YoloV5 model in the two different ways:

  1. We can train the YoloV5 model and use it for object detection in images as well as videos.
  2. We can train the YoloV5 model on the custom data set. We can train a YoloV5 model with a custom data set of our own choice. For example: If we want to detect only one object like a car, we can train the YoloV5 model with the car annotated data set.
  3. And I have already explained in part 1 . What is annotation for object detection? If you don’t read that post, so can read the previous post before reading this.

Note: If you haven’t read my previous post about the data set preparation and annotation of the data set. Just go and read that post before reading it.

Visit website to read complete tutorial : https://www.datascience2000.in/2021/06/object-detection-using-yolov5-part-2.html

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