Instructions to use RumiaKit/swin.ham.glide-finetuned-SkinDisease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RumiaKit/swin.ham.glide-finetuned-SkinDisease with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="RumiaKit/swin.ham.glide-finetuned-SkinDisease") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("RumiaKit/swin.ham.glide-finetuned-SkinDisease") model = AutoModelForImageClassification.from_pretrained("RumiaKit/swin.ham.glide-finetuned-SkinDisease", device_map="auto") - Notebooks
- Google Colab
- Kaggle
swin.ham.glide-finetuned-SkinDisease
This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2642
- Accuracy: 0.9071
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.8772 | 1.0 | 117 | 0.5824 | 0.7722 |
| 2.2544 | 2.0 | 234 | 0.3814 | 0.8691 |
| 1.6324 | 3.0 | 351 | 0.3674 | 0.8691 |
| 1.6009 | 4.0 | 468 | 0.3168 | 0.8831 |
| 1.3198 | 5.0 | 585 | 0.3410 | 0.8871 |
| 1.1221 | 6.0 | 702 | 0.3018 | 0.8931 |
| 1.0570 | 7.0 | 819 | 0.2712 | 0.9071 |
| 0.9043 | 8.0 | 936 | 0.2730 | 0.9081 |
| 0.7277 | 9.0 | 1053 | 0.2851 | 0.9011 |
| 0.4737 | 10.0 | 1170 | 0.2642 | 0.9071 |
Framework versions
- Transformers 5.5.4
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
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Model tree for RumiaKit/swin.ham.glide-finetuned-SkinDisease
Base model
microsoft/swin-base-patch4-window7-224Evaluation results
- Accuracy on imagefoldertest set self-reported0.907