HAMMALE/vit-tiny-classifier-rvlcdip

ViT-Tiny Classifier for RVL-CDIP Document Classification (Distilled)

This model is a compressed Vision Transformer (ViT-Tiny) trained using knowledge distillation from DiT-Large on the RVL-CDIP dataset for document image classification. This model was developed as part of a research internship at the Laboratory of Complex Systems, Ecole Centrale Casablanca

Model Details

• Student Model: ViT-Tiny (Vision Transformer)
• Teacher Model: microsoft/dit-large-finetuned-rvlcdip
• Training Method: Knowledge Distillation
• Parameters: ~5.5M (55x smaller than teacher)
• Dataset: RVL-CDIP (320k document images, 16 classes)
• Task: Document Image Classification
• Accuracy: 0.9210
• Compression Ratio: ~55x parameter reduction from teacher model

Document Classes

The model classifies documents into 16 categories:

1. letter - Personal or business correspondence
2. form - Structured forms and applications
3. email - Email communications
4. handwritten - Handwritten documents
5. advertisement - Marketing materials and ads
6. scientific_report - Research reports and studies
7. scientific_publication - Academic papers and journals
8. specification - Technical specifications
9. file_folder - File folders and organizational documents
10. news_article - News articles and press releases
11. budget - Financial budgets and planning documents
12. invoice - Bills and invoices
13. presentation - Presentation slides
14. questionnaire - Surveys and questionnaires
15. resume - CVs and resumes
16. memo - Internal memos and notices

Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

# Load model
processor = AutoImageProcessor.from_pretrained("HAMMALE/vit-tiny-classifier-rvlcdip")
model = AutoModelForImageClassification.from_pretrained("HAMMALE/vit-tiny-classifier-rvlcdip")

# Load and classify an image
image = Image.open("path_to_your_document_image.jpg")
inputs = processor(image, return_tensors="pt")

# Get predictions
outputs = model(**inputs)
predicted_class_id = outputs.logits.argmax(-1).item()

# Get class names
class_names = [
    "letter", "form", "email", "handwritten", "advertisement", 
    "scientific_report", "scientific_publication", "specification", 
    "file_folder", "news_article", "budget", "invoice", 
    "presentation", "questionnaire", "resume", "memo"
]

predicted_class = class_names[predicted_class_id]
print("Predicted class:", predicted_class)

Performance

Metric	Value
Accuracy	0.9210
Parameters	~5.5M
Model Size	~22 MB
Input Size	224x224 pixels

Training Details

• Student Architecture: Vision Transformer (ViT-Tiny)
• Teacher Model: microsoft/dit-large-finetuned-rvlcdip
• Distillation Method: Knowledge Distillation
• Input Resolution: 224x224
• Preprocessing: Standard ImageNet normalization
• Framework: Transformers/PyTorch
• Distillation Benefits: Maintains high accuracy with 55x fewer parameters

Dataset

The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset contains:

• 400,000 grayscale document images
• 16 document categories
• Images collected from truth tobacco industry documents
• Standard train/validation/test splits

Citation

@misc{hammale2025vit_tiny_rvlcdip_distilled,
  title={ViT-Tiny Classifier for RVL-CDIP Document Classification (Distilled)},
  author={Hammale, Mourad},
  year={2025},
  howpublished={\url{https://huggingface.co/HAMMALE/vit-tiny-classifier-rvlcdip}},
  note={Knowledge distilled from microsoft/dit-large-finetuned-rvlcdip}
}

Acknowledgments

This model was created by HAMMALE (Mourad) through knowledge distillation from the larger DiT-Large model (microsoft/dit-large-finetuned-rvlcdip), achieving significant compression while maintaining competitive performance for document classification tasks.

License

This model is released under the Apache 2.0 license.