---
license: agpl-3.0
tags:
  - yolov12
  - object-detection
  - watermark-detection
  - dinov3
  - vision-transformer
  - pytorch
  - ultralytics
pipeline_tag: object-detection
library_name: ultralytics
language: en
datasets:
  - custom
metrics:
  - mAP
  - precision
  - recall
model-index:
  - name: yolov12x-dino3-watermark-detection
    results:
      - task:
          type: object-detection
        metrics:
          - type: mAP50
            value: 84.1
          - type: mAP50-95
            value: 59.9
          - type: precision
            value: 91.8
          - type: recall
            value: 74.2
---

# 🔍 YOLOv12x-DINOv3 Watermark Detection Model

<div align="center">

[![Python](https://img.shields.io/badge/Python-3.8+-3776ab?logo=python&logoColor=white)](https://python.org)
[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org)
[![License](https://img.shields.io/badge/License-AGPL--3.0-blue.svg)](LICENSE)
[![Ultralytics](https://img.shields.io/badge/Ultralytics-YOLOv12-00FFFF?logo=yolo&logoColor=white)](https://ultralytics.com)

**State-of-the-art watermark detection powered by YOLOv12 + DINOv3 Vision Transformers**

</div>

## 📊 Model Performance

| Metric | Value |
|--------|-------|
| **mAP@0.5** | 84.1% |
| **mAP@0.5:0.95** | 59.9% |
| **Precision** | 91.8% |
| **Recall** | 74.2% |

## 🏗️ Architecture

- **Base Model**: YOLOv12x (Extra-Large variant)
- **Enhancement**: DINOv3 ViT-B/16 backbone integration
- **Configuration**: Dual P0/P3 feature enhancement
- **Input Size**: 1024×1024

### Key Features

- 🧠 **DINOv3 Vision Transformer** integration at P4 level (40×40×512)
- 🔄 **Dual-scale feature fusion** for improved small/medium object detection
- ⚡ **Optimized for watermark detection** with high precision
- 🛡️ **Production-ready** with comprehensive error handling

## 🚀 Quick Start

### Installation

```bash
pip install ultralytics
```

### Inference

```python
from ultralytics import YOLO

# Load model from Hugging Face
model = YOLO('hf://corzent/yolov12x-dino3-watermark-detection')

# Or load locally
model = YOLO('best.pt')

# Run inference
results = model('image.jpg')

# Process results
for result in results:
    boxes = result.boxes
    for box in boxes:
        print(f"Watermark detected: confidence {box.conf[0]:.2f}")
```

### Batch Processing

```python
# Process multiple images
results = model(['image1.jpg', 'image2.jpg', 'image3.jpg'])

# Process video
results = model('video.mp4', stream=True)
```

## 📋 Training Details

| Parameter | Value |
|-----------|-------|
| **Epochs** | 100 |
| **Batch Size** | 2 |
| **Image Size** | 1024×1024 |
| **Optimizer** | SGD |
| **Learning Rate** | 0.01 → 0.0001 |
| **Momentum** | 0.937 |
| **Weight Decay** | 0.0005 |
| **Augmentation** | RandAugment, MixUp (0.2), Mosaic |

## 📈 Training Progress

The model was trained for 100 epochs with consistent improvement:

- **Epoch 1**: mAP50 = 0.3%
- **Epoch 25**: mAP50 = 53.7%
- **Epoch 50**: mAP50 = 77.2%
- **Epoch 75**: mAP50 = 79.6%
- **Epoch 100**: mAP50 = **84.1%**

## 🔧 Model Configuration

The model uses the `yolov12x-dino3-vitb16-dual.yaml` configuration:

```yaml
# YOLOv12x with DINOv3 ViT-B/16 Dual Enhancement
backbone:
  - YOLOv12x backbone layers
  - DINOv3 ViT-B/16 integration at P4

head:
  - Multi-scale detection head
  - Dual P0/P3 feature enhancement
```

## 📁 Files Included

- `best.pt` - Best model checkpoint (highest mAP)
- `last.pt` - Final epoch checkpoint
- `args.yaml` - Training configuration
- `results.csv` - Training metrics log
- `*.png` - Training curves and confusion matrices

## ⚠️ Limitations

- Optimized specifically for watermark detection
- Requires GPU with 8GB+ VRAM for inference at 1024×1024
- Best performance on images similar to training distribution

## 📄 License

This model is released under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html).

## 🙏 Acknowledgments

- [**Sompote/DINOV3-YOLOV12**](https://github.com/Sompote/DINOV3-YOLOV12) - Original YOLOv12 + DINOv3 integration
- [Ultralytics](https://ultralytics.com/) - YOLO framework
- [Meta AI](https://github.com/facebookresearch/dinov2) - DINOv2/v3 Vision Transformers
- [PyTorch](https://pytorch.org/) - Deep learning framework

## 📞 Citation

If you use this model, please cite the original YOLOv12-DINOv3 repository:

```bibtex
@software{sompote_yolov12_dinov3_2024,
  title={YOLOv12 + DINOv3 Vision Transformers Integration},
  author={Sompote},
  year={2024},
  url={https://github.com/Sompote/DINOV3-YOLOV12}
}
```