datasets:
- pierreguillou/DocLayNet-base
metrics:
- accuracy
base_model:
- facebook/deit-base-distilled-patch16-224
library_name: transformers
tags:
- vision
- document-layout-analysis
- document-classification
- deit
- doclaynet
Data-efficient Image Transformer(DeiT) for Document Classification(DocLayNet)
This model is a fine-tuned Data-efficient Image Transformer(DeiT) for document image classification based on the DocLayNet dataset.
Trained on images of the document categories from DocLayNet dataset where the categories namely(with their indexes) are :
{'financial_reports': 0, 'government_tenders': 1, 'laws_and_regulations': 2, 'manuals': 3, 'patents': 4, 'scientific_articles': 5}
Model description
DeiT(facebook/deit-base-distilled-patch16-224) finetuned on document classification
Training data
DocLayNet-base https://huggingface.co/datasets/pierreguillou/DocLayNet-base
Training procedure
hyperparameters:
{ 'batch_size': 128, 'num_epochs': 20, 'learning_rate': 1e-4, 'weight_decay': 0.1, 'warmup_ratio': 0.1, 'gradient_clip': 0.1, 'dropout_rate': 0.1, 'label_smoothing': 0.1 'optmizer': 'AdamW' }
Evaluation results
Test Loss: 0.8134, Test Acc: 81.56%
Usage
from transformers import pipeline
# Load the model using the image-classification pipeline
pipe = pipeline("image-classification", model="kaixkhazaki/vit_doclaynet_base")
# Test it with an image
result = pipe("path_to_image.jpg")
print(result)