Skin Lesion Classifiers

--- 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.

Source context: 0 downloads · 0 likes · Pipeline image-classification · Library pytorch · Repo diddoe/skin-lesion-classifiers