Image Classification
Transformers
PyTorch
TensorBoard
English
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/vit-base-patch16-224-in21k_GI_diagnosis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/vit-base-patch16-224-in21k_GI_diagnosis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DunnBC22/vit-base-patch16-224-in21k_GI_diagnosis") 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("DunnBC22/vit-base-patch16-224-in21k_GI_diagnosis") model = AutoModelForImageClassification.from_pretrained("DunnBC22/vit-base-patch16-224-in21k_GI_diagnosis") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01c78217070c6106b45f687c02b47920556e1382866b7bea55559165c650f57c
- Size of remote file:
- 343 MB
- SHA256:
- 7c91d860f434433d875a73e8b8ab28b0cce8194b4882b64440b8975a926fae19
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