Instructions to use e1010101/vit-384-tongue-image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use e1010101/vit-384-tongue-image with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="e1010101/vit-384-tongue-image") 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("e1010101/vit-384-tongue-image") model = AutoModelForImageClassification.from_pretrained("e1010101/vit-384-tongue-image") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
A fine-tune of Google's ViT-384 model for multi-label image classification on tongue images.
Model Details
Model Description
The model will predict the presence/absence of three features; Cracks, Red Dots and Toothmarks.
- Model type: Vision Transformer
- Finetuned from model [optional]: https://huggingface.co/google/vit-base-patch16-384
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