Classification vs Detection vs Segmentation Models: Differences and When to Use Each

Image classification, object detection, and segmentation are key techniques in computer vision that allow machines to interpret and analyze visual data. Image classification involves assigning labels to an entire image or video to identify what is present, such as differentiating between a beach and a pool. Object detection takes this a step further by identifying and localizing specific objects within an image using bounding boxes, which is useful for identifying individual elements like cars on a street. Segmentation goes beyond detection by labeling each pixel in an image, providing detailed outlines of objects, which is particularly beneficial for isolating objects from their backgrounds, such as tagging a shirt in a fashion photo. These techniques collectively enhance computer vision capabilities, enabling machines to process and understand visual information more like humans, thanks to advancements in machine learning and artificial intelligence.

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