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license: apache-2.0
library_name: pytorch
pipeline_tag: image-classification
tags:
- vision-transformer
- pytorch
- computer-vision
- image-classification
- document-classification
- identity-documents
- multi-task-learning
- vit
---
# Multi-Head Vision Transformer for Indian Identity Document Classification
> **Document Classification Module of the LLaVA ID Extraction Project**
This repository contains a fine-tuned Vision Transformer (ViT-Base) model that simultaneously predicts:
- 📄 Document Type
- 🪪 Document Side (Front / Back)
- 🗺️ Issuing State
using a shared Vision Transformer backbone with three independent classification heads.
This model serves as the **first component** of the larger **LLaVA ID Extraction** project, whose goal is to build an end-to-end AI pipeline for automated identity document understanding, information extraction, and verification.
---
# Project Overview
Current Pipeline
```
Identity Document
│
▼
Multi-Head Vision Transformer ✅
│
├── Document Type
├── Front / Back
└── State
│
▼
OCR (Planned)
│
▼
LLaVA Information Extraction (Planned)
│
▼
Identity Verification (Planned)
│
▼
Fraud Detection (Planned)
```
---
# Model Details
## Backbone
- Google ViT Base Patch16 224
## Architecture
Shared Vision Transformer encoder with three task-specific classification heads:
1. Document Type Classification
2. Document Side Classification
3. State Classification
## Framework
- PyTorch
- Hugging Face Transformers
---
# Dataset
The model was trained on a synthetic dataset containing Indian identity document images with annotations for:
- Document Type
- Document Side
- Issuing State
The dataset includes multiple document categories and image variations suitable for supervised multi-task learning.
---
# Training Configuration
| Parameter | Value |
|-----------|------|
| GPU | NVIDIA Tesla T4 |
| Epochs | 4 |
| Optimizer | AdamW |
| Scheduler | Cosine Annealing |
| Mixed Precision | FP16 |
| Image Size | 224 × 224 |
| Backbone | ViT Base Patch16 224 |
---
# Results
| Task | Accuracy |
|------|---------:|
| Document Type | **100%** |
| Document Side | **100%** |
| State | **100%** |
| Combined Prediction | **100%** |
These results were obtained on the held-out validation split of the synthetic dataset.
---
# Repository Contents
```
multihead_vit_best.pth
label_mappings.json
training.ipynb
```
---
# Loading the Model
```python
import torch
model.load_state_dict(
torch.load(
"multihead_vit_best.pth",
map_location="cpu"
)
)
model.eval()
```
---
# Future Roadmap
This model represents the **document classification module** of the broader **LLaVA ID Extraction** project.
Future additions include:
- OCR integration
- LLaVA-based information extraction
- Identity verification
- Document forgery detection
- Fraud risk analysis
- Interactive Gradio demo
---
# Limitations
The model was trained exclusively on synthetic identity document images.
Although it achieves excellent performance on the held-out synthetic validation set, additional fine-tuning and evaluation on real-world document scans and photographs are recommended before production deployment.
---
# GitHub
GitHub repository:
**https://github.com/IshankAggarwal09/llava_id_extraction** |