Image Classification
Transformers
PyTorch
TensorBoard
English
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use DunnBC22/dit-base-Document_Classification-RVL_CDIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DunnBC22/dit-base-Document_Classification-RVL_CDIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DunnBC22/dit-base-Document_Classification-RVL_CDIP") 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/dit-base-Document_Classification-RVL_CDIP") model = AutoModelForImageClassification.from_pretrained("DunnBC22/dit-base-Document_Classification-RVL_CDIP") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bfb9a36bf184747f0d0ea53c3c4418f9f6745af739b87b531678594324ae9539
- Size of remote file:
- 343 MB
- SHA256:
- 2f95e2477b609f61de21f40f675d6659fc13e50384353e45d156eb830841a61d
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