What is ResNet?

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🔹 What is ResNet?

ResNet (Residual Network) is a type of deep neural network architecture introduced by Microsoft Research in 2015 (He et al., "Deep Residual Learning for Image Recognition"). It was revolutionary because it solved the problem of training very deep networks.

🔹 The Core Idea: Residual Learning

• In very deep networks, as more layers are added, performance often degrades due to vanishing/exploding gradients.

• ResNet introduced skip (shortcut) connections that let information bypass certain layers.

• Instead of directly learning a mapping $H(x)$, a residual block learns $F(x) = H(x) - x$.

• This makes optimization easier because the network only needs to learn the “residual” (the difference).

🔹 Architecture

• Residual Block:

  $$Output = F(x) + x$$

  where $F(x)$ is the transformation (convolutions, activation, etc.), and $x$ is the input.

• These blocks can be stacked to form very deep networks like ResNet-50, ResNet-101, ResNet-152 (numbers indicate layer depth).

🔹 Why ResNet is Important

1. Enabled training of very deep networks (100+ layers).

2. Improved accuracy in image recognition tasks (won ImageNet 2015).

3. Became the foundation for many modern architectures (DenseNet, EfficientNet).

4. Works extremely well in computer vision tasks: classification, detection, segmentation.

🔹 Applications

• Image classification (e.g., identifying objects in photos).

• Object detection (used in Faster R-CNN, YOLO variants).

• Medical imaging (detecting diseases from X-rays, MRIs).

• Feature extraction for transfer learning.

👉 In short:
ResNet is a deep convolutional neural network that uses residual connections (shortcuts) to train extremely deep models efficiently, making it a backbone of modern computer vision.

Read more:

Explain transfer learning in vision tasks.

What is object detection vs image classification?

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