Real-Time Object Detection using YoloV7 on Google Colab

Author(s): Adijsad

Originally published on Towards AI.

Want to test your video using Yolov7 and Google Colab? Learn how to make real-time object detection using your videos in this tutorial

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YOLO (you only look once) is one of the state-of-the-art object detection models, overpowering most of its rivals.

How does it work?

If you want to know how YOLO works in a couple of paragraphs, it goes like this:

Firstly, training data is composed of images, bounding box vectors (i.e., [Pc Bx By Bw Bh C1 C2 ….]), with each vector representing a box around a known object in the image. Thus, images with four known objects in it will have four such vectors, and with seven will have seven vectors.

In vector [Pc Bx By Bw Bh C1 C2 …] (Bounding box representation), Pc represents the probability of a particular class that the box portrays, Bx & By represent the center coordinates of the box and Bw & Bh are width and height respectively. [C1, C2 …] count corresponds to the number of classes present in the dataset.

Since it is essential to have a fixed length vector to train a neural network model, the key feature of the YOLO model is to divide the image into NxN grid where each grid cell produces B bounding boxes of… Read the full blog for free on Medium.

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Published via Towards AI