Image Classification vs. Object Detection
Distinguish between the terms image classification and object detection.
We'll cover the following
Image classification
Image classification is the task of assigning a label or class to an entire image. The main goal is to create a model that can tell us what it sees in a given input image. A dog? A bird? A table?
Image classification tasks, which are very common to be used alone, can also be part of more complex tasks, such as object detection.
Object Detection
While the image classification problem focuses on classifying the images, there can be more than one object we are searching for in an image; our task is to find all of them placed in the most appropriate boxes with their belonging classes. Our goal is relatively more complex than image classification. We have to first detect the objects by locating them with boxes, then classify them to decide which class this box belongs to. To sum up:
Image classification: Predicting the class of an image.
Object localization: Locating the presence of objects in an image and indicating their location with a bounding box.
Object detection: Locating the presence of objects with a bounding box and detecting the classes of the located objects in these boxes.
Being in the computer vision category, both image classification and object detection have a wide range of use cases. From medical imaging to autonomous driving—in other words, whatever we want to automize using visual scenes.
For example, in an autonomous driving project, we could use image classification to decide if the current traffic light is red, yellow, or green. It would give us the lead to stop or pass. On the other hand, by taking pictures from the front view and applying object detection, we could recognize and locate pedestrians, cyclists, or other cars to lead our next move.
