Types of Computer Vision Models and Applications

Different types of Computer Vision Models

1. Classification: It is used to determine the "class" of the output. It typically returns Yes/No depending upon Input amount thousands of classes.

2. Detection: This model is used to find object and their location in the input image. It return the X and Y coordinate of the input image.

The various models such as Car detection in the traffic comes under this category.

3. Segmentation: This model is used to classify each and every pixel of the image. It helps to check the every minute input of the image.

It can easily understand Simple Class Vs. Not a Class Vs. Tens of Class. It is used in post-processing and can remove or smoothen a very small area.

It is of two types:

1. Semantic Segmentation: In this all the objects of same classes are one.

2. Instance Segmentation: In this all the objects of a class are separate.

The framework developed using these models are:

1. SSD is a object detection network that combines classification with object detection through use of default boundary boxes at different layers and network levels.

2. RESNET utilizes residual layer to skip over section of layers and avoid vanishing gradient problem with deep neural network.

3. Mobile Net utilized the layers like 1*1 convolutions to cut down on computational complexity and network size leading to fast inference without substantial decrease in accuracy.