Explain the difference between YOLO and R-CNN.

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YOLO (You Only Look Once) and R-CNN (Region-based Convolutional Neural Network) are both object detection models, but they work very differently.

R-CNN follows a two-stage approach. First, it generates multiple region proposals in an image (possible object locations). Then, each region is passed through a CNN for classification and bounding box prediction. While accurate, R-CNN is slow because it processes thousands of proposals individually. Variants like Fast R-CNN and Faster R-CNN improved speed by sharing convolutional features and using a Region Proposal Network (RPN), but they are still relatively heavy.

YOLO, on the other hand, uses a single-stage approach. It divides the image into a grid and predicts bounding boxes and class probabilities directly in one pass through the network. This makes YOLO extremely fast and suitable for real-time detection tasks like self-driving cars or surveillance. However, earlier YOLO versions were slightly less accurate for detecting small objects compared to R-CNN.

In summary:

• R-CNN = slower, two-step, high accuracy.

• YOLO = faster, one-step, real-time friendly.

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