Explain the difference between classification, detection, segmentation, and recognition. in computer vision

📝 Computer Vision Tasks: Classification vs. Detection vs. Segmentation vs. Recognition

1. Image Classification

• What it does: Identifies the main object or scene in an image.
• Output: A single label (class).
• Example: Input → Image of a cat; Output → "Cat"
• Use Case: Photo tagging, disease diagnosis from X-rays.
• Model: VGG, ResNet, EfficientNet.

2. Object Detection

• What it does: Locates and identifies multiple objects in an image.
• Output: Labels + bounding boxes for each object.
• Example: Input → Street image; Output → [{"label": "car", "bbox": [...]}, {"label": "person", "bbox": [...]}]
• Use Case: Self-driving cars, surveillance systems.
• Model: YOLO, Faster R-CNN, SSD.

3. Semantic Segmentation

• What it does: Classifies every pixel in the image.
• Output: Pixel-wise class labels (same label for same-class objects).
• Example: Input → Cityscape image; Output → Each pixel labeled as "road", "sky", "building", etc.
• Use Case: Medical imaging, autonomous navigation.
• Model: U-Net, DeepLab, FCN.

4. Instance Segmentation

• What it does: Like semantic segmentation but differentiates between instances of the same class.
• Output: Pixel-wise labels with unique IDs per object.
• Example: Two dogs in image → dog1 and dog2 are separately labeled.
• Use Case: Crowd analysis, robotics.
• Model: Mask R-CNN.

5. Object Recognition

• What it does: Recognizes specific identity of an object/person.
• Output: Identity of the object or person.
• Example: Input → Face image; Output → "Barack Obama"
• Use Case: Face unlock, biometric security.
• Model: FaceNet, ArcFace, Siamese Networks.

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