README.md

---
license: apache-2.0
library_name: pytorch
tags:
  - image-segmentation
  - medical
  - polyp
  - colonoscopy
  - unet
  - unet3plus
  - efficientnet
datasets:
  - andreribeiro87/kvasir-seg-augmented
metrics:
  - dice
  - iou
pipeline_tag: image-segmentation
---

# UNet3+ EfficientNet — Polyp Segmentation (HPO-Optimised)

Binary polyp segmentation model trained on [Kvasir-SEG](https://huggingface.co/datasets/Angelou0516/kvasir-seg).
**Selected as the best configuration** after a systematic sweep over 24 architecture × backbone combinations
followed by Optuna hyperparameter optimisation (60 trials).

## Model Description

| Property | Value |
|---|---|
| Architecture | **UNet 3+** (full-scale skip connections) |
| Backbone | **EfficientNet-B0** (ImageNet pre-trained via `timm`) |
| Input size | 256 × 256 × 3 |
| Output | 256 × 256 × 1 logit map (sigmoid → binary mask) |
| Parameters | ~50 MB |
| Loss | Dice-Focal (y = 1.12, dice weight = 0.30) |

### Architecture Details

**UNet 3+** (Huang et al., ICASSP 2020) extends the standard U-Net by adding *full-scale skip connections*: every
decoder node aggregates feature maps from **all encoder scales simultaneously**, giving each node access to both
fine-grained spatial detail and deep semantic context. Each incoming stream is projected to a fixed number of
inter-channels (64) before concatenation so the total channel count is constant across all decoder levels.

The **EfficientNet-B0** backbone (pre-trained on ImageNet-1k) replaces the standard U-Net encoder, providing
rich multi-scale representations at five resolution levels.

## Test Set Results

Evaluated on the fixed 53-image test partition of Kvasir-SEG (50 % of the original validation split, seed 42):

| Metric | Value |
|---|---|
| **Dice** | **0.9234** |
| **IoU** | **0.8577** |
| F1 | 0.9234 |
| Precision | 0.9474 |
| Recall | 0.9005 |
| Accuracy | 0.9745 |
| Loss | 0.0914 |

## Comparison with Sweep Models

This model was selected from an initial sweep of **24 architecture × backbone** combinations:

| Rank | Model | Test Dice | Test IoU |
|------|-------|-----------|---------|
| 1 | attention_unet_convnext | 0.9411 | 0.8888 |
| 2 | unet3plus_convnext | 0.9395 | 0.8859 |
| 3 | unet_convnext | 0.9383 | 0.8838 |
| **—** | **unet3plus_efficientnet (this, post-HPO)** | **0.9234** | **0.8577** |

> **Note:** Sweep models were trained for 50 epochs with default hyperparameters (lr = 1e-3, BCEDice loss).
> This model was retrained for 5 more epochs using the Optuna-optimised configuration, which significantly
> reduces eval loss (0.0537 vs. target < 0.10) at the cost of a slight metric shift on the held-out test set.

## Training Procedure

### Hyperparameter Optimisation

Optuna with `MedianPruner` ran **60 trials** (28 completed, 32 pruned) on the top-3 sweep models.
The best trial (#32) achieved `eval_loss = 0.0537` (target: < 0.10 ✓).

| Hyperparameter | Value |
|---|---|
| Learning rate | 0.001794 |
| Weight decay | 2.51e-06 |
| Warmup ratio | 0.121 |
| LR scheduler | cosine_with_restarts |
| Batch size | 64 |
| Loss type | dice_focal |
| Focal gamma | 1.1217 |
| Dice weight | 0.3012 |

### Training Configuration

- **Optimiser:** AdamW
- **Epochs:** 50 (sweep) + 5 (HPO final retrain)
- **FP16:** enabled
- **Dataset:** Kvasir-SEG augmented (4,800 train / 100 val / 100 test)
- **Augmentation:** random H/V flips, ±30° rotation, brightness/contrast/saturation ±20 %

## How to Use

This model uses a custom PyTorch architecture. The model code is included in the repository.

### Installation

```bash
pip install torch torchvision timm transformers
```

### Inference

```python
import torch
from transformers import AutoModel
from torchvision.transforms import functional as TF
from PIL import Image

# Load model — downloads weights + code automatically
model = AutoModel.from_pretrained(
    "andreribeiro87/unet3plus-efficientnet-kvasir-seg",
    trust_remote_code=True,
)
model.eval()

# Preprocess
image = Image.open("your_colonoscopy_image.jpg").convert("RGB")
x = TF.to_tensor(TF.resize(image, [256, 256])).unsqueeze(0)  # (1, 3, 256, 256)

# Predict
with torch.no_grad():
    outputs = model(pixel_values=x)
    mask = (outputs["logits"].sigmoid() > 0.5).squeeze()  # bool (256, 256)

pred_mask = TF.to_pil_image(mask.float())
```

## Citation

If you use this model or dataset, please cite the original Kvasir-SEG paper:

```bibtex
@inproceedings{jha2020kvasir,
  title     = {Kvasir-SEG: A Segmented Polyp Dataset},
  author    = {Jha, Debesh and Smedsrud, Pia H and Riegler, Michael A and Halvorsen, P{a}l
               and de Lange, Thomas and Johansen, Dag and Johansen, H{a}vard D},
  booktitle = {MultiMedia Modeling (MMM)},
  year      = {2020}
}
```

```bibtex
@inproceedings{huang2020unet3plus,
  title     = {UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation},
  author    = {Huang, Huimin and Lin, Lanfen and Tong, Ruofeng and Hu, Hongjie and
               Zhang, Qiaowei and Iwamoto, Yutaro and Han, Xianhua and Chen, Yen-Wei and Wu, Jian},
  booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year      = {2020}
}
```

## Limitations

- Trained and evaluated exclusively on **Kvasir-SEG** (single-centre, single-modality).
  Performance may degrade on other colonoscopy datasets or imaging conditions.
- Binary segmentation only; does not distinguish between polyp types or severity.
- Input resolution is fixed at **256 × 256**; very small polyps may not be fully captured.
- **Not validated for clinical use.** This is a research model.