Unet Kvasir Seg

Input: 3x256x256 RGB image -> Output: 1x256x256 segmentation mask

Loss Function

BCE + Dice Loss -- Binary Cross-Entropy provides smooth per-pixel gradients, while Dice Loss directly optimizes mask overlap and handles class imbalance (polyps are typically small relative to background).

Training

• 20 epochs, batch size 8, learning rate 1e-4 (Adam)
• Trained on Kvasir-SEG dataset (gastrointestinal polyp segmentation)
• Best checkpoint selected by validation loss

Parameters

~31M parameters

--- tags: - image-segmentation - medical-imaging - unet - pytorch datasets: - Angelou0516/kvasir-seg --- # U-Net for Gastrointestinal Polyp Segmentation ## Architecture Standard U-Net with: - **Encoder**: 4 levels (64, 128, 256, 512 channels), each with two Conv3x3+BN+ReLU blocks followed by MaxPool2x2 - **Bottleneck**: 1024 channels at 16x16 spatial resolution - **Decoder**: 4 levels mirroring the encoder, using ConvTranspose2d for learned upsampling + skip connections via concatenation - **Output**: 1x1 Conv producing a single-channel binary mask Input: 3x256x256 RGB image -> Output: 1x256x256 segmentation mask ## Loss Function **BCE + Dice Loss** -- Binary Cross-Entropy provides smooth per-pixel gradients, while Dice Loss directly optimizes mask overlap and handles class imbalance (polyps are typically small relative to background). ## Training - 20 epochs, batch size 8, learning rate 1e-4 (Adam) - Trained on Kvasir-SEG dataset (gastrointestinal polyp segmentation) - Best checkpoint selected by validation loss ## Parameters ~31M parameters