ModelPublicadoExterno

Kvasir Seg Unet

Binary segmentation model for gastrointestinal polyp detection, trained on the Kvasir-SEG dataset.

Fuentehuggingface.co/violetar/kvasir-seg-unet/tree/18468bb6ba2a9f11f103bdf6442e7eb36b5205fd

TipoOtherVersiónRevision 18468bb

Fuente del modelo

Tipo: Other
Tamaño del archivo: 181 MB
Versión: vRevision 18468bb
Actualizado: 27 sept 2026
Descargas: 0
Tareas: Segmentación de imágenes
Fuente: Hugging Face

Descripción de la fuente

ResNet-50 U-Net — Kvasir-SEG Polyp Segmentation

Binary segmentation model for gastrointestinal polyp detection, trained on the Kvasir-SEG dataset.

Architecture

A U-Net with a ResNet-50 encoder pretrained on ImageNet.

Component	Details
Encoder	ResNet-50 (ImageNet pretrained), all layers fine-tuned
Skip connections	Concatenation of encoder and decoder feature maps at each scale
Decoder	Transposed-conv ×2 upsampling + double conv block at each level
Output	Single-channel logit map (1 × H × W)

Feature map sizes for a 256×256 input:

stride 2 ->  64ch, 128×128
stride 4 -> 256ch, 64×64
stride 8 -> 512ch, 32×32
stride 16 -> 1024ch, 16×16
stride 32 -> 2048ch, 8×8 (bottleneck)

Loss Function

Medical image segmentation suffers from severe class imbalance (background pixels vastly outnumber polyp pixels). To address this, the model is trained with a combined loss:

Loss = 0.5 × Focal + 0.5 × Tversky

Loss	Parameters	Role
Focal	α = 0.8, γ = 2	Down-weights easy background pixels; focuses learning on hard examples
Tversky	α = 0.3, β = 0.7	Penalises false negatives more than false positives, improving recall on small polyps

Training

Dataset: 880 train / 60 val / 60 test (Kvasir-SEG split)
Input size: 256×256, normalised with ImageNet mean/std
Batch size: 8
Epochs: 20
LR schedule: Cosine decay with 10% linear warm-up
Hyperparameter search: Optuna over learning_rate {5e-4, 1e-3} and weight_decay {1e-4, 1e-3}
Best weights: snapshot of the epoch with highest validation Dice across all trials

Data Augmentation (train only)

Spatial (applied identically to image and mask): Random horizontal/vertical flip, ±20° rotation, affine (translate ±5%, scale 0.9–1.1), elastic deformation (α=60, σ=6)

Photometric (image only, p=0.8): Color jitter (brightness/contrast ±0.3, saturation ±0.2, hue ±0.05), Gaussian blur (3×3, σ∈[0.1, 2.0])

Usage

from model import ResNet50UNet
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download

model = ResNet50UNet(num_classes=1, pretrained=False)
weights_path = hf_hub_download(repo_id="VioletaR/kvasir-seg-unet", filename="model.safetensors")
model.load_state_dict(load_file(weights_path))
model.eval()

Inputs must be 256×256 RGB tensors normalised with ImageNet statistics (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]). The model outputs raw logits; apply torch.sigmoid and threshold at 0.5 for the binary mask.

Fuentes

1 fuente

Hugging FacePrincipalVersión 18468bb6ba2a9f11f103bdf6442e7eb36b5205fd

huggingface.co/violetar/kvasir-seg-unet/tree/18468bb6ba2a9f11f103bdf6442e7eb36b5205fd

Verificado 27 sept

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 181 MB · SHA-256 c22eec27e31d…d51d · Hugging Face

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Extractos de fuentes

2 extractos

Readme27 septhuggingface.co/violetar/kvasir-seg-unet

--- language: en license: mit tags: - image-segmentation - medical-imaging - unet - pytorch datasets: - Angelou0516/kvasir-seg metrics: - dice - iou --- # ResNet-50 U-Net — Kvasir-SEG Polyp Segmentation Binary segmentation model for gastrointestinal polyp detection, trained on the [Kvasir-SEG](https://huggingface.co/datasets/Angelou0516/kvasir-seg) dataset. ## Architecture A U-Net with a **ResNet-50 encoder** pretrained on ImageNet. | Component | Details | |---|---| | Encoder | ResNet-50 (ImageNet pretrained), all layers fine-tuned | | Skip connections | Concatenation of encoder and decoder feature maps at each scale | | Decoder | Transposed-conv ×2 upsampling + double conv block at each level | | Output | Single-channel logit map (1 × H × W) | Feature map sizes for a 256×256 input: ``` stride 2 -> 64ch, 128×128 stride 4 -> 256ch, 64×64 stride 8 -> 512ch, 32×32 stride 16 -> 1024ch, 16×16 stride 32 -> 2048ch, 8×8 (bottleneck) ``` ## Loss Function Medical image segmentation suffers from severe class imbalance (background pixels vastly outnumber polyp pixels). To address this, the model is trained with a **combined loss**: ``` Loss = 0.5 × Focal + 0.5 × Tversky ``` | Loss | Parameters | Role | |---|---|---| | **Focal** | α = 0.8, γ = 2 | Down-weights easy background pixels; focuses learning on hard examples | | **Tversky** | α = 0.3, β = 0.7 | Penalises false negatives more than false positives, improving recall on small polyps | ## Training - **Dataset:** 880 train / 60 val / 60 test (Kvasir-SEG split) - **Input size:** 256×256, normalised with ImageNet mean/std - **Batch size:** 8 - **Epochs:** 20 - **LR schedule:** Cosine decay with 10% linear warm-up - **Hyperparameter search:** Optuna over `learning_rate` {5e-4, 1e-3} and `weight_decay` {1e-4, 1e-3} - **Best weights:** snapshot of the epoch with highest validation Dice across all trials ### Data Augmentation (train only) **Spatial** (applied identically to image and mask): Random horizontal/vertical flip, ±20° rotation, affine (translate ±5%, scale 0.9–1.1), elastic deformation (α=60, σ=6) **Photometric** (image only, p=0.8): Color jitter (brightness/contrast ±0.3, saturation ±0.2, hue ±0.05), Gaussian blur (3×3, σ∈[0.1, 2.0]) ## Usage ```python from model import ResNet50UNet from safetensors.torch import load_file from huggingface_hub import hf_hub_download model = ResNet50UNe...

Note27 septhuggingface.co/violetar/kvasir-seg-unet

Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Repo violetar/kvasir-seg-unet