Kvasir Polyp Segmentation
Two TensorFlow/Keras models for gastrointestinal polyp segmentation on endoscopy images, trained on the Kvasir-SEG dataset augmented with normal (no-polyp) images from Kvasir v2.
Source du modèle
- Type
- UNet
- Taille du fichier
- 38,7–356 MB
- Version
- vDeeplabv3plus best
- Mis à jour
- 1 nov. 2026
- Source
- Hugging Face
Description de la source
Kvasir Polyp Segmentation (U-Net & DeepLabV3+)
Two TensorFlow/Keras models for gastrointestinal polyp segmentation on endoscopy images, trained on the 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
ReduceLROnPlateauandEarlyStopping). - 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:
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.
Sources
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Sources
1 sourceVérifié 1 nov.
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Extraits de sources
2 extraits--- 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 met...
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library keras · Repo bekmon/kvasir-polyp-segmentation
