---
language: en
license: apache-2.0
tags:
- vision
- image-classification
- document-classification
- knowledge-distillation
- vit
- rvl-cdip
- tiny-model
- distilled-model
datasets:
- rvl_cdip
metrics:
- accuracy
pipeline_tag: image-classification

---

# 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

```python
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

```bibtex
@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.