Instructions to use Nitishshiremath/skin_cancer_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nitishshiremath/skin_cancer_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Nitishshiremath/skin_cancer_model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Nitishshiremath/skin_cancer_model") model = AutoModelForImageClassification.from_pretrained("Nitishshiremath/skin_cancer_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
skin_cancer_model
This model is a fine-tuned version of google/vit-base-patch16-224 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2884
- Accuracy: 0.8845
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3049 | 1.0 | 264 | 0.2884 | 0.8845 |
| 0.0922 | 2.0 | 528 | 0.3536 | 0.9053 |
| 0.0088 | 3.0 | 792 | 0.3536 | 0.9148 |
| 0.0003 | 4.0 | 1056 | 0.3854 | 0.9148 |
| 0.0003 | 5.0 | 1320 | 0.3842 | 0.9167 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Nitishshiremath/skin_cancer_model
Base model
google/vit-base-patch16-224