Week04 Polyp Segmentation Unet Multiclass
Multi-class (3-class) segmentation model trained on the Kvasir-SEG dataset.
Model source
- Kind
- Other
- File size
- 119 MB
- Version
- vRevision 1cf2eee
- Updated
- Sep 27, 2026
- Downloads
- 0
- Tasks
- Image segmentation
- Source
- Hugging Face
Source description
U-Net for Gastrointestinal Polyp Segmentation (Multi-Class)
Multi-class (3-class) segmentation model trained on the Kvasir-SEG dataset.
See also the binary segmentation variant.
Architecture
Same U-Net encoder-decoder as the binary variant, with output changed to 3 channels:
- Encoder: 4 stages (64 → 128 → 256 → 512) + 1024-channel bottleneck.
- Decoder: 4 upsampling stages with skip connections (512 → 256 → 128 → 64).
- Reduction head: Conv2d(64→32) + BatchNorm + ReLU before the final 1×1 conv.
- Output: 3-channel logits (multi-class segmentation).
- Input: 3-channel RGB, 256×256.
Loss Function
CombinedLoss = 0.5 × CrossEntropy + 0.5 × Multi-class Dice Loss
- CrossEntropy: replaces BCE for the multi-class case, providing per-pixel classification gradients across all classes.
- Multi-class Dice Loss: computes per-class Dice using softmax probabilities and one-hot targets, then averages across classes. Ensures balanced optimization even with class imbalance.
Training Setup
| Parameter | Value |
|---|---|
| Epochs | 20 |
| Optimizer | Adam |
| Learning rate | 1e-4 |
| LR scheduler | ReduceLROnPlateau (factor=0.5, patience=3, min_lr=1e-6) |
| Batch size | 8 |
| Image size | 256×256 |
| GPU | NVIDIA GeForce RTX 3080 Ti |
| Dataset splits | train=800, val=100, test=100 |
Metrics
| Split | Dice Coefficient | Loss |
|---|---|---|
| Validation (best) | 0.9036 | — |
| Test | 0.9169 | 0.3322 |
Usage
import torch
from model import UNet
from safetensors.torch import load_file
model = UNet(in_channels=3, num_classes=3)
model.load_state_dict(load_file("model.safetensors"))
model.eval()
# input: (B, 3, 256, 256) float tensor in [0, 1]
# output: (B, 3, 256, 256) logits — apply argmax(dim=1) for class map
Or download from Hub:
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from model import UNet
weights = hf_hub_download(
repo_id="sebastiao-teixeira/week04-polyp-segmentation-unet-multiclass",
filename="model.safetensors",
)
model = UNet(in_channels=3, num_classes=3)
model.load_state_dict(load_file(weights))
model.eval()
Sources
1 source
Sources
1 sourceVerified Sep 27
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: mit tags: - image-segmentation - medical-imaging - polyp-segmentation - unet - pytorch - multi-class datasets: - Angelou0516/kvasir-seg pipeline_tag: image-segmentation --- # U-Net for Gastrointestinal Polyp Segmentation (Multi-Class) Multi-class (3-class) segmentation model trained on the [Kvasir-SEG](https://huggingface.co/datasets/Angelou0516/kvasir-seg) dataset. See also the [binary segmentation variant](https://huggingface.co/sebastiao-teixeira/week04-polyp-segmentation-unet). ## Architecture Same U-Net encoder-decoder as the binary variant, with output changed to 3 channels: - **Encoder**: 4 stages (64 → 128 → 256 → 512) + 1024-channel bottleneck. - **Decoder**: 4 upsampling stages with skip connections (512 → 256 → 128 → 64). - **Reduction head**: Conv2d(64→32) + BatchNorm + ReLU before the final 1×1 conv. - **Output**: 3-channel logits (multi-class segmentation). - Input: 3-channel RGB, 256×256. ## Loss Function **CombinedLoss** = 0.5 × CrossEntropy + 0.5 × Multi-class Dice Loss - **CrossEntropy**: replaces BCE for the multi-class case, providing per-pixel classification gradients across all classes. - **Multi-class Dice Loss**: computes per-class Dice using softmax probabilities and one-hot targets, then averages across classes. Ensures balanced optimization even with class imbalance. ## Training Setup | Parameter | Value | |---|---| | Epochs | 20 | | Optimizer | Adam | | Learning rate | 1e-4 | | LR scheduler | ReduceLROnPlateau (factor=0.5, patience=3, min_lr=1e-6) | | Batch size | 8 | | Image size | 256×256 | | GPU | NVIDIA GeForce RTX 3080 Ti | | Dataset splits | train=800, val=100, test=100 | ## Metrics | Split | Dice Coefficient | Loss | |---|---|---| | Validation (best) | 0.9036 | — | | Test | 0.9169 | 0.3322 | ## Usage ```python import torch from model import UNet from safetensors.torch import load_file model = UNet(in_channels=3, num_classes=3) model.load_state_dict(load_file("model.safetensors")) model.eval() # input: (B, 3, 256, 256) float tensor in [0, 1] # output: (B, 3, 256, 256) logits — apply argmax(dim=1) for class map ``` Or download from Hub: ```python from huggingface_hub import hf_hub_download from safetensors.torch import load_file from model import UNet weights = hf_hub_download( repo_id="sebastiao-teixeira/week04-polyp-segmentation-unet-multiclass", filename="model.safetensors", ) m...
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo sebastiao-teixeira/week04-polyp-segmentation-unet-multiclass
