---
license: mit
tags:
- image-classification
- skin-lesion
- dermatology
- pytorch
- kan
- vision-transformer
pipeline_tag: image-classification
library_name: pytorch
---

# Skin Lesion Classification: CNN and KAN-ViT Hybrids

Trained checkpoints for six image classifiers on a 14-class skin condition
dataset. Two are custom CNNs trained from scratch. Four are KAN-ViT hybrids that
pair a pretrained backbone with Kolmogorov-Arnold Network (KAN) spline layers.

Code, training notebooks, and full documentation:
https://github.com/deettoh/skin-lesion-classification

## Checkpoints

| File | Model | Val acc | Test acc |
|------|-------|---------|----------|
| `custom_cnn_v1/v1_custom_cnn_skin_lesion_100_epochs.pth` | Custom CNN V1 | 87.19% | 87.7% |
| `custom_cnn_v2/v2_custom_cnn_skin_lesion_100_epochs.pth` | Custom CNN V2 | 86.72% | — |
| `kanvit/kan_vit_skin_lesion_30_epochs.pth` | KAN-ViT | 80.03% | 79.62% |
| `efficientnetb0_kanvit/efficientnetb0_kan_vit_skin_lesion_35_epochs.pth` | EfficientNetB0-KANViT | 81.99% | 83.01% |
| `b0_kanvit_with_mlp_unfrozen_backbone/efficientnetb0_kan_vit_mlp_hybrid_skin_lesion_50_epochs.pth` | EfficientNetB0-KANViT-MLP | 81.39% | 82.46% |
| `efficientnetb3_kanvit_mlp/efficientnetb3_kan_vit_skin_lesion_50_epochs.pth` | EfficientNetB3-KANViT-MLP | 84.40% | 85.44% |

Val acc is best validation accuracy from the training logs. Test acc is held-out
test accuracy, with test-time augmentation for the CNN.

## Usage

```python
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    "diddoe/skin-lesion-classifiers",
    "custom_cnn_v1/v1_custom_cnn_skin_lesion_100_epochs.pth",
)
```

Rebuild the matching architecture from the
[GitHub repo](https://github.com/deettoh/skin-lesion-classification), then load
with `model.load_state_dict(torch.load(path))`. All models output 14 classes.

## Dataset

Trained on the Kaggle dataset `ahmedxc4/skin-ds`, a 14-class set of dermoscopy
and clinical skin images. The distribution is long-tailed, so every run uses a
weighted sampler and Custom CNN V2 uses focal loss.

## License

MIT for the model weights. The training images carry their own upstream terms,
including ISIC archive sources. Check those before redistributing data.