With the introduction of deep convolutional networks (CNN) for ImageNet classification, deep learning had a significant impact on artificial intelligence in computer vision applications synthesis. We used deep learning to solve the following computer vision issues.
Our understanding of computer vision thus far has been limited to its ability to accept an image as input and produce an output that can be classified into a certain item category.
An image of just one thing, such as a photograph, is the input.
A class label is a result.
Because a picture can be classified into multiple categories, it is the most challenging aspect of computer vision. The most common dataset in this context, ImageNet, contains millions of categorized photos. Picture classification determines the class of an object or adds a class label to an image.
Deep learning follows CIFAR and makes use of the idea of photo categorization datasets. Convolutional neural networks that recognize images more accurately are made possible via deep learning. Similar to how neurons function in the human brain, this network does.
Image Classification With Localization
A class label and the position of the object in the image are assigned as part of image classification with localization. It surrounds the thing with a bounding box. It is more difficult, though, as it can need to place bounding boxes around several variations of the same object in the image.
The next stage after object classification is object detection. The same image is given several instances of the same class or various classes in this instance. The problem of object detection is more difficult since various types of photos may contain several items.
Object segmentation, or semantic segmentation
An image is divided into segments by image segmentation. Although object segmentation is another name for object detection. However, bounding boxes are not used in object segmentation. Instead, it pinpoints the exact picture pixels that make up the item.