| --- |
| 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. |
|
|
