Object recognition is to describe a collection of related computer vision tasks that involve activitieslike identifying objects in digital photographs. Image classification involves activities such aspredicting the class of one object in an image. Object localization is refers to identifying the locationof one or more objects in an image and drawing an abounding box around their extent. Objectdetection does the work of combines these two tasks and localizes and classifies one or more objectsin an image. When a user or practitioner refers to the term “object recognition“, they often mean“object detection“. It may be challenging for beginners to distinguish between different relatedcomputer vision tasks.So, we can distinguish between these three computer vision tasks with this example:Image Classification: This is done by Predict the type or class of an object in an image.Input: An image which consists of 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: This is done through, Locate the presence of objects in an image and indicatetheir location with a bounding box.Input: An image which consists of 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: This is done through, Locate the presence of objects with a bounding box and typesor classes of the located objects in an image.Input: An image which consists of one or more objects, such as a photograph.Output: One or more bounding boxes (e.g. defined by a point, width, and height), and a class label foreach bounding box.One of the further extension to this breakdown of computer vision tasks is object segmentation, alsocalled “object instance segmentation” or “semantic segmentation,” where instances of recognizedobjects are indicated by highlighting the specific pixels of the object instead of a coarse bounding box.From this breakdown, we can understand that object recognition refers to a suite of challengingcomputer vision tasks.For example, image classification is simply straight forward, but the differences between objectlocalization and object detection can be confusing, especially when all three tasks may be just asequally referred to as object recognition.Humans can detect and identify objects present in an image. The human visual system is fast andaccurate and can also perform complex tasks like identifying multiple objects and detect obstacleswith little conscious thought. The availability of large sets of data, faster GPUs, and better algorithms,we can now easily train computers to detect and classify multiple objects within an image with highaccuracy. We need to understand terms such as object detection, object localization, loss function forobject detection and localization, and finally explore an object detection algorithm known as “Youonly look once” (YOLO).Image classification also involves assigning a class label to an image, whereas object localizationinvolves drawing a bounding box around one or more objects in an image. Object detection is alwaysmore challenging and combines these two tasks and draws a bounding box around each object ofinterest in the image and assigns them a class label. Together, all these problems are referred to asobject recognition.Object recognition refers to a collection of related tasks for identifying objects in digital photographs.Region-based Convolutional Neural Networks, or R-CNNs, is a family of techniques for addressingobject localization and recognition tasks, designed for model performance. You Only Look Once, orYOLO is known as the second family of techniques for object recognition designed for speed andreal-time use.