polyp-unet-r34-heavy
U-Net with a ResNet34 encoder, trained for polyp segmentation on the standard benchmark (900 Kvasir-SEG + 550 CVC-ClinicDB) and evaluated on five centres across three countries.
Training recipe: heavy. Random seed: 3.
This is the median seed, not the best
5 models were trained with this recipe, differing only in random seed. Their scores on ETIS-Larib ranged from 0.7144 to 0.7328. This checkpoint is the median of those 5, deliberately.
Publishing the best of 5 would contradict the finding the project reports: that seed variance on this benchmark is large enough to manufacture apparent improvements. A model card showing a cherry-picked run would be an example of the problem, not a result.
Performance
| test set | centre | this checkpoint | 5-seed mean ± std |
|---|---|---|---|
| Kvasir | seen | 0.8947 | 0.8989 ± 0.0088 |
| CVC-ClinicDB | seen | 0.9063 | 0.8952 ± 0.0088 |
| CVC-ColonDB | unseen | 0.7549 | 0.7597 ± 0.0091 |
| CVC-300 | unseen | 0.8672 | 0.8597 ± 0.0243 |
| ETIS-LaribPolypDB | unseen | 0.7198 | 0.7211 ± 0.0074 |
Dice, computed per image and averaged. "Seen" means the centre contributed images to training. Evaluation is at 352×352 rather than native resolution, which caps achievable Dice at roughly 0.97 and makes these numbers not exactly comparable to published results.
Usage
import torch
import segmentation_models_pytorch as smp
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
path = hf_hub_download("saadaamir14/polyp-unet-r34-heavy", "model.safetensors")
model = smp.Unet("resnet34", encoder_weights=None, in_channels=3, classes=1)
model.load_state_dict(load_file(path))
model.eval()
# input: RGB, resized to 352x352, normalised with ImageNet statistics
# output: one logit per pixel; sigmoid then threshold at 0.5
Limitations
This model is a research artifact. It is not a medical device and must not be used for clinical decisions.
- Trained on 1,305 images from two centres. Performance at a new hospital is not predictable from these numbers.
- Drops roughly 0.2 Dice moving from centres seen in training to an unseen centre in another country.
- Small polyps are the dominant failure mode. At 352×352 the smallest ETIS polyp occupies 133 pixels, and the network downsamples 32×.
- CVC-ClinicDB scores are optimistic; that dataset's train and test frames come from overlapping colonoscopy videos.
Full protocol, statistics and the complete limitations list: https://github.com/saad-aamir/polyp-segmentation
Citation
Datasets belong to their original authors. Please cite Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, CVC-300 and ETIS-Larib if you use them.