Coding Club Of Competitive Programmers

Objective: Classify the objects using deep learning techniques. Theory: Image classification involves assigning a class label to an image, whereas object localization involves drawing a bounding box around one or more objects in an image. Object detection is more challenging and combines these two tasks and draws a bounding box around each object of interest in the image and assigns them a class label. Together, all of these problems are referred to as object recognition. Image Classification: Predict the type or class of an object in an image. Input: An image with a single object, such as a photograph. Output: A class label (e.g. one or more integers that are mapped to class labels). Object Localization: Locate the presence of objects in an image and indicate their location with a bounding box. Input: An image with one or more objects, such as a photograph. Output: One or more bounding boxes (e.g. defined by a point, width, and height). Object Detection: Locate the presence of objects...