The U-Net architecture, primarily used for image segmentation, employs a characteristic filter shape within its convolutional layers. Here's a breakdown:
Core Concepts:
- Convolutional Layers: U-Net relies heavily on convolutional layers, which use filters (also called kernels) to extract features from input images.
- Filter Shape: The filter shape refers to the spatial dimensions of the kernel. For example, a 3x3 filter means the kernel has a width and height of 3 pixels.
- U-Net Structure: U-Net has a contracting path (encoder) and an expanding path (decoder). Both paths consist of convolutional layers.
Typical U-Net Filter Shape:
- The most common filter shape used in U-Net is 3x3. This means that each convolutional layer uses 3x3 kernels.
- These 3x3 filters are applied with a stride of 1 and "same" padding, which ensures that the output feature maps have the same spatial dimensions as the input feature maps.
- It is not uncommon to see other filter sizes, but 3x3 is by far the most common.
Why 3x3?
- Local Features: 3x3 filters capture local features in the image, which are often sufficient for segmentation tasks.
- Computational Efficiency: 3x3 filters are computationally efficient compared to larger filters, reducing the number of parameters and computations.
- Stacking Layers: Stacking multiple 3x3 convolutional layers allows the network to learn increasingly complex features.
Variations:
- While 3x3 is standard, some U-Net variants might use other filter sizes, such as 1x1 convolutions for channel-wise operations or 5x5 convolutions for capturing larger spatial contexts.
- In 3D U-Nets, the kernels are 3 dimensional, so the most common kernal size would be 3x3x3.
In summary: The standard U-Net architecture predominantly uses 3x3 convolutional filters, which strike a balance between capturing local features and computational efficiency.
🧠3x3 filter shape
https://gist.github.com/viadean/82f9a8c3f6a9350248e1b4a66a663ff2
Explanation: