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:
- e417248d87905fb77e97f60e7f1eb92e1c2868809542d04be01d2f51caf003f3
- Size of remote file:
- 343 MB
- SHA256:
- 0d22b4d4e3f41ebc8881bbe62f3e0893ce9273655dcd8f1e3f04caf7fcec1213
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