---
license: mit
tags:
  - image-segmentation
  - medical-imaging
  - polyp-segmentation
  - gastrointestinal
  - tensorflow
  - keras
  - unet
  - deeplabv3plus
library_name: keras
pipeline_tag: image-segmentation
---

# Kvasir Polyp Segmentation (U-Net & DeepLabV3+)

Two TensorFlow/Keras models for **gastrointestinal polyp segmentation** on endoscopy images, trained on the [Kvasir-SEG](https://huggingface.co/datasets/kowndinya23/Kvasir-SEG) dataset augmented with normal (no-polyp) images from Kvasir v2.

## Models

| File | Architecture | Backbone | Size |
|------|--------------|----------|------|
| `unet_best.h5` | U-Net | from scratch | ~373 MB |
| `deeplabv3plus_best.h5` | DeepLabV3+ | EfficientNet-B0 (ImageNet) | ~40 MB |

Both are saved as **full Keras models** (architecture + weights) in legacy HDF5 format.

## Intended use

- **Input:** RGB image resized to **256x256**, pixel values normalized to **[0, 1]**.
- **Output:** single-channel **sigmoid** mask of shape `(256, 256, 1)`. Threshold at **0.5** to obtain a binary polyp mask.

## Training data

- **Positives:** Kvasir-SEG — 1,000 polyp images with manually annotated segmentation masks.
- **Negatives:** ~500 normal images from Kvasir v2 (`normal-cecum`, `normal-pylorus`, `normal-z-line`), paired with all-zero masks so the model learns that "no polyp" is a valid output and avoids false positives on healthy tissue.

## Training setup

- **Loss:** Dice + Binary Cross-Entropy (BCE). The BCE term keeps gradients well-behaved on empty (normal) masks, where pure Dice is unstable.
- **Optimizer:** Adam, learning rate `1e-4`.
- **Batch size:** 8. **Epochs:** 15 (with `ReduceLROnPlateau` and `EarlyStopping`).
- **Metrics:** Dice coefficient, IoU (on polyp images); sensitivity / specificity / false-positive rate (across polyp vs. normal images).

## How to load

These models use custom loss/metric functions. For **inference only**, skip them with `compile=False`:

```python
from tensorflow.keras.models import load_model
from huggingface_hub import hf_hub_download

path = hf_hub_download("bekmon/kvasir-polyp-segmentation", "unet_best.h5")
model = load_model(path, compile=False)

# preprocess: RGB -> 256x256 -> /255.0, shape (1, 256, 256, 3)
pred = model.predict(image_batch)        # (1, 256, 256, 1), values in [0, 1]
mask = (pred > 0.5).astype("float32")    # binary polyp mask
```

To resume training (or use the custom metrics), pass `custom_objects` with `dice_bce_loss`, `dice_loss`, `bce_loss`, `dice_coef`, and `iou_coef`.

## Limitations

- Trained on single-center data (Kvasir / Bærum Hospital); may not generalize to other scopes or populations.
- Positives and negatives come from related but distinct Kvasir collections, so some dataset-bias ("shortcut learning") risk remains.
- Not a medical device. For research and educational use only.