Introduction to Computer Vision with PyTorch (1/6)

Computer Vision (CV) is a field that studies how computers can gain some degree of understanding from digital images and/or video. Understanding in this definition has a rather broad meaning — it can range from being able to distinguish between a cat and a dog on the picture, to more complex tasks such as describing the image in natural language. The most common problems of computer vision include: <ul> <li>Image Classification is the simplest task, when we need to classify an image into one of many pre-defined categories, for example, distinguish a cat from a dog on a photograph, or recognize a handwritten digit.</li> <li>Object Detection is a bit more difficult task, in which we need to find known objects on the picture and localize them, that is, return the bounding box for each of recognized objects.</li> <li>Segmentation is similar to object detection, but instead of giving bounding box we need to return an exact pixel map outlining each of the recognized objects.</li> </ul> We’ll focus on image classification task, and how neural networks can be used to solve it. As with any other machine learning tasks, to train a model for classifying images we’ll need a labeled dataset, that is, a large number of images for each of the classes. <a href="https://medium.com/@thevnotebook/introduction-to-computer-vision-with-pytorch-1-6-40915fbb97ec">Click Here</a>