---
license: apache-2.0
base_model: PekingU/rtdetr_v2_r101vd
tags:
- object-detection
- computer-vision
- voucher-classification
- rt-detr
- rtdetrv2
datasets:
- custom-voucher-dataset
metrics:
- map
- map_50
- map_75
widget:
- src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg
  example_title: Example Image
---

# RT-DETRv2 Fine-tuned for Voucher Classification

This model is a fine-tuned version of [PekingU/rtdetr_v2_r101vd](https://huggingface.co/PekingU/rtdetr_v2_r101vd) for voucher classification and object detection.

## Model Details

### Model Description
- **Model Type**: Object Detection (RT-DETRv2)
- **Base Model**: PekingU/rtdetr_v2_r101vd
- **Task**: Multi-class voucher classification and detection
- **Classes**: 3 classes
  - 0: digital (digital invoices)
  - 1: fisico (physical receipts on blank pages)
  - 2: tesoreria (small on-site payment receipts)

### Training Details

**Training Dataset:**
- **Total Samples**: 507
- **Class Distribution**:
- **fisico** (id: 1): 241 samples (47.5%)
- **digital** (id: 0): 147 samples (29.0%)
- **tesoreria** (id: 2): 119 samples (23.5%)


**Training Configuration:**
- **Image Size**: 800x800
- **Batch Size**: 24
- **Learning Rate**: 1.5e-05
- **Weight Decay**: 0.0001
- **Epochs**: 2
- **Validation Split**: 0.0

**Data Processing:**
- Pre-augmented dataset used (no runtime augmentation)
- External train/validation split (use create_train_val_split.py)
- Preprocessing: Resize + Normalization only

### Performance Metrics

**Final Evaluation Results:**
**Dataset Information:**
*Training Dataset:*
- **Digital invoices**: 147 samples (29.0%)
- **Fisico receipts**: 241 samples (47.5%)
- **Tesoreria receipts**: 119 samples (23.5%)
- **Total training samples**: 507

**Model Configuration:**
- **Base model**: PekingU/rtdetr_v2_r101vd
- **Architecture**: rtdetr_v2_r101vd
- **Input resolution**: 800×800 pixels
- **Training epochs**: 2
- **Batch size**: 24

**Training Hardware:**
- **GPU**: NVIDIA A100-SXM4-40GB
- **VRAM**: 39.6 GB
- **RAM**: 83.5 GB
- **GPU configuration**: A100 optimized

**Training Time**: 0.0 minutes

**Training Summary:**
- **Final training loss**: 0.0000


### MLflow Tracking

- **MLflow Run ID**: c348e8235f8c40138c05c051fc207bb6
- **MLflow Experiment**: RT-DETRv2_Voucher_Classification


## Usage

```python
from transformers import AutoModelForObjectDetection, AutoImageProcessor
import torch
from PIL import Image
import numpy as np

# Load model and processor
model = AutoModelForObjectDetection.from_pretrained("jnmrr/rtdetr-v2-voucher-classifier")
image_processor = AutoImageProcessor.from_pretrained("jnmrr/rtdetr-v2-voucher-classifier")

# Load and preprocess image
image = Image.open("path/to/your/voucher.jpg").convert("RGB")
inputs = image_processor(images=image, return_tensors="pt")

# Run inference
with torch.no_grad():
    outputs = model(**inputs)

# Post-process results
target_sizes = torch.tensor([image.size[::-1]])  # (height, width)
results = image_processor.post_process_object_detection(
    outputs, 
    target_sizes=target_sizes, 
    threshold=0.5
)[0]

# Print predictions
class_names = ["digital", "fisico", "tesoreria"]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    print(f"Class: {class_names[label.item()]}")
    print(f"Confidence: {score.item():.3f}")
    print(f"BBox: {box.tolist()}")
```

## Training Procedure

The model was fine-tuned using the Hugging Face Transformers library with:
- Pre-augmented dataset focusing on challenging cases
- Format-specific augmentation strategies applied during data preparation
- MLflow experiment tracking for reproducibility
- External train/validation split for unbiased evaluation

## Limitations and Bias

- Trained specifically on voucher/receipt images
- Performance may vary on images significantly different from training distribution
- Model optimized for 3-class voucher classification task

## Citation

If you use this model, please cite:

```bibtex
@misc{rtdetr-v2-voucher-classifier,
  title={RT-DETRv2 Fine-tuned for Voucher Classification},
  author={Your Name},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/jnmrr/rtdetr-v2-voucher-classifier}
}
```