What are the differences between segmentation models?

What are the differences between segmentation models?

Segmentation models are used to partition images into meaningful segments. Common types include U-Net, Mask R-CNN, and DeepLab. Each has unique architectures and performance metrics.

Can you explain how U-Net works?

U-Net is an encoder-decoder architecture that captures context and enables precise localization. It uses skip connections to combine features from the encoder with the decoder.

What are the advantages of using Mask R-CNN?

Mask R-CNN extends Faster R-CNN by adding a branch for predicting segmentation masks on each Region of Interest, allowing for instance segmentation.

How does DeepLab handle different scales?

DeepLab uses atrous convolution to capture multi-scale context by adjusting the convolutional kernel's field of view without losing resolution.

Which model is best for real-time applications?

For real-time applications, models like MobileNetV2 or EfficientDet are often preferred due to their lightweight architecture and faster inference times.