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diddoe/
skin-lesion-classifiers

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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, 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.