Instructions to use JuIm/ViT-Skin-Cancer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuIm/ViT-Skin-Cancer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="JuIm/ViT-Skin-Cancer") 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("JuIm/ViT-Skin-Cancer") model = AutoModelForImageClassification.from_pretrained("JuIm/ViT-Skin-Cancer") - Notebooks
- Google Colab
- Kaggle
vit-base-skin
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset.
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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
- 3
Model tree for JuIm/ViT-Skin-Cancer
Base model
google/vit-base-patch16-224-in21k