FolashadeO/week4

This model is a compact PyTorch U-Net for binary gastrointestinal polyp segmentation on the Kvasir-SEG dataset.

Architecture

The network uses a four-level U-Net with convolutional encoder blocks, a bottleneck, transposed-convolution decoder blocks, skip connections, and a final 1 x 1 convolution that produces one mask logit channel.

Loss

Training uses a combined Dice and binary cross-entropy loss. BCE handles per-pixel classification, while Dice loss directly optimizes mask overlap and helps with foreground/background class imbalance.