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unet-kvasir-seg
rmachado23/unet-kvasir-seg
unet-kvasir-seg is a image segmentation model from rmachado23. Use it for the image segmentation task on the model card, and read the license before you ship it in a product.
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…
Access
Public
Updated Mar 9, 2026
Parameters
31.1M
124 MB on disk
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At a glance
- Task
- Image Segmentation
- Access
- Public
- Created
- Mar 9, 2026
- Updated
- Mar 9, 2026
- SHA
- 5e1711ad
Datasets
Model card
From the Hugging Face model README
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
- Task
- Image Segmentation
- Created
- Mar 9, 2026
- Updated
- Mar 9, 2026