Instructions to use microsoft/dit-base-finetuned-rvlcdip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/dit-base-finetuned-rvlcdip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/dit-base-finetuned-rvlcdip") 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("microsoft/dit-base-finetuned-rvlcdip") model = AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip") - Inference
- Notebooks
- Google Colab
- Kaggle
Finetuned model availability for document parsing(OCR) or Key Information Extraction
#3
by stray-light - opened
The original paper mentions good performance on FUNSD text parsing "where Mask R-CNN is used with different backbones
(ResNeXt, DeiT, BEiT, MAE and DiT)" . Is the finetuned model available on Huggingface? How to fine-tune for my own task?