Object detection using YoloV5 (Part 1)



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Object detection using YoloV5 (Part 1)

Annotation for object detection (YoloV5):

In this tutorial, we are going to see that how to prepare the data set for the object detection using YoloV5. And in the next tutorial, we will learn that how to train the YoloV5 model on the custom data set.

Annotation is a method for creating the bounding boxes around the objects in the images. Bounding boxes is one of the most popular and recognizable image annotation methods used in deep learning and machine learning. Using the image annotation method, we create the outlines (bounding boxes) around the objects in the images as per the requirement of our project. For example: if we want to detect the car. So we make a bounding box around the car like there is one image and there are 2 cars in the image. So we create the 2 bounding boxes around the car.

Download the data set before annotation, we are using here a two wheeler and four wheeler vehicle data set. So you can download the car, bike and scooter dataset, then you need to annotate. I’ll provide the dataset for testing.

Go to the app.roboflow.com website using the above link and create an account with your personal Gmail id or you can create an account using the GitHub account.

We can apply annotations using different tools like LabelImg. But personally, I suggest a website for annotating the data set called app.roboflow.com. This website helps you to annotate the data set in different formats like txt, csv, json etc. You can visit the website using the link below.

Visit website to read complete tutorial : https://www.datascience2000.in/2021/06/object-detection-using-yolov5-part-1.html

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