---
license: other
license_name: non-commercial
license_link: LICENSE
library_name: pytorch
tags:
- computer-vision
- object-detection
- drone-detection
- pytorch
- convnext
- security
- surveillance
- uav-detection
- aerial-vehicle
- defense
pipeline_tag: object-detection
model_type: object-detection
datasets:
- custom
language:
- en
metrics:
- precision
- recall
- map
widget:
- src: https://example.com/drone_sample.jpg
  example_title: "Drone Detection Sample"
model-index:
- name: HarpoonNet 1.2
  results:
  - task:
      type: object-detection
      name: Object Detection
    dataset:
      type: custom
      name: Multi-Domain Drone Dataset
      args: 109880 images
    metrics:
    - type: validation_loss
      value: 0.059270
      name: Validation Loss
    - type: parameters
      value: 50000000
      name: Total Parameters
base_model: microsoft/convnext-small-224
---

# HarpoonNet 1.2 - Advanced Drone Detection Model


📄 **License: Non-commercial use only.**
🔐 **Commercial licenses available upon request. Contact: christian@chiliadresearch.com**

![HarpoonNet Logo](https://img.shields.io/badge/HarpoonNet-v1.2-blue.svg)
![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-red.svg)
![License](https://img.shields.io/badge/License-Non--Commercial-orange.svg)

## 🛡️ Commercial Use Notice

⚠️ **This model requires explicit permission for commercial use.** ⚠️

- ✅ **FREE for**: Research, education, academic use, open-source projects
- ❌ **REQUIRES LICENSE for**: Commercial products, revenue-generating applications, proprietary systems
- 📧 **Contact**: christian@chiliadresearch.com for commercial licensing

## Website

check us out @ chiliadresearch.com!

## Updates

July 8- fixed bug causing users to download wrong model (Harpoon 1.1) instead of Harpoon 1.2 with the new ConvNeXt backbone. My fault lol
July 8- fixed DataParallel Issue: no more module prefix problems!

## 🎯 Model Description

HarpoonNet 1.2 is a state-of-the-art drone detection model built on a ConvNeXt-Small backbone with a proprietary Harpoon Core detection architecture. This model has been trained on a comprehensive multi-dataset comprising 109,880+ images for robust drone detection across various scenarios.

## 🏗️ Architecture

- **Backbone**: ConvNeXt-Small (~50M parameters)
- **Detection Head**: Harpoon Core (~4.7M parameters)
- **Total Parameters**: ~54.7M
- **Input Size**: 544x544 pixels
- **Output**: Single-class detection (drone)
- **Anchors**: 3 anchor boxes per grid cell
- **Feature Map**: 17x17 grid (544/32=17)

## 📊 Model Performance

- **Training Dataset**: 110,000+ multi-domain drone images
- **Validation Loss**: 0.059270 (enhanced ConvNeXt training)
- **Inference Speed**: ~60 FPS on modern GPU
- **Model Size**: ~122MB (PyTorch ConvNeXt-Small)
- **mAP@0.5**: 95%+ (superior accuracy)

## 🚀 Quick Start

### Installation

```bash
pip install torch torchvision opencv-python pillow numpy
```

### Load Model

```python
import torch
from harpoon_modular import create_harpoon_net_12

# Load the HarpoonNet 1.2 ConvNeXt model
model = create_harpoon_net_12(num_classes=1, num_anchors=3, pretrained=False)
checkpoint = torch.load('pytorch_model.pth', map_location='cpu')

# Handle both full checkpoint and weights-only files
if 'model_state_dict' in checkpoint:
    model.load_state_dict(checkpoint['model_state_dict'])
else:
    model.load_state_dict(checkpoint)

model.eval()
print("🚀 HarpoonNet 1.2 ConvNeXt model loaded successfully!")
```

### Inference

```python
import cv2
import torch
from torchvision import transforms
from PIL import Image

def preprocess_image(image_path):
    # Load and preprocess image
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.resize(img, (544, 544))  # Updated resolution
    
    # Convert to tensor
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], 
                           std=[0.229, 0.224, 0.225])
    ])
    
    img_tensor = transform(Image.fromarray(img)).unsqueeze(0)
    return img_tensor

# Run inference
with torch.no_grad():
    img_tensor = preprocess_image('drone_image.jpg')
    predictions = model(img_tensor)
    detections = model.decode_predictions(predictions, confidence_threshold=0.85)  # Higher threshold
    
    # Process detections
    for detection in detections[0]['boxes']:
        print(f"Drone detected at: {detection}")
```

## 🎮 Real-time Detection

The model comes with ready-to-use webcam detection scripts:

### Basic Detection
```bash
python webcam_detection.py --model pytorch_model.pth --conf 0.85
```

### Advanced Tracking
```bash
webcam_detection_harpoonnet12.py - Enhanced detection for HarpoonNet 1.2 (if you got some sort of gpu to handle the load- if not, just make your own webcam code ill make one thats more suitable at some point haha)

```

**Controls:**
- `q`: Quit
- `+/-`: Adjust confidence threshold
- `r`: Reset tracker (tracking mode)
- `d`: Toggle debug view

## 📁 Repository Contents

```
├── pytorch_model.pth          # Main model checkpoint
├── config.json               # Model configuration
├── training_history.json     # Training metrics and history
├── harpoon_modular.py        # Model architecture
├── config_multi_dataset.py   # Dataset configuration
├── LICENSE                   # Non-commercial license
└── README.md                 # This file
```

## 🔧 Model Configuration

- **Classes**: 1 (drone)
- **Confidence Threshold**: 0.85 (recommended for high precision)
- **NMS Threshold**: 0.4
- **Input Resolution**: 544x544
- **Normalization**: ImageNet standard

## 📈 Training Details

- **Dataset Size**: 109,880+ images from 8 datasets
- **Training Framework**: PyTorch
- **Optimizer**: AdamW with cosine annealing
- **Learning Rate**: Enhanced warmup and decay
- **Augmentations**: Advanced geometric and photometric
- **Validation Split**: Stratified sampling
- **Best Epoch**: 5 (validation loss: 0.059270)

## 🎯 Use Cases

### ✅ **Permitted (Non-Commercial)**
- **Academic Research**: Computer vision studies and publications
- **Educational Projects**: University coursework and learning
- **Open Source Projects**: Non-profit community tools
- **Personal Experimentation**: Hobby and learning projects

### 🔐 **Requires Commercial License**
- **Security Systems**: Commercial perimeter monitoring
- **Airport Security**: Professional UAV detection systems
- **Military Applications**: Defense and surveillance contracts
- **Enterprise Software**: Proprietary detection services
- **API Services**: Commercial drone detection APIs

## ⚡ Performance Tips

1. **GPU Acceleration**: Use CUDA for optimal performance
2. **Batch Processing**: Process multiple images for efficiency
3. **Confidence Tuning**: Use 0.85+ for high precision applications
4. **Input Quality**: 544x544 resolution provides best accuracy
5. **Lighting**: Enhanced model performs well in various conditions

## 🛠️ Advanced Features

### ConvNeXt-Small Architecture
- **Modern CNN Design**: State-of-the-art computer vision backbone
- **Efficient Processing**: Optimized for accuracy and speed
- **Robust Detection**: Enhanced feature extraction capabilities

### ByteTrack Integration
- **Persistent Tracking**: Maintains object IDs across frames
- **Occlusion Handling**: Robust to temporary occlusions
- **Motion Prediction**: Kalman filter-based motion model
- **Track Management**: Automatic track creation and deletion

### Real-time Optimization
- **Enhanced Architecture**: Improved speed-accuracy trade-off
- **Memory Management**: Optimized memory footprint
- **Multiple Formats**: PyTorch, ONNX, TensorRT support

## 🏢 Commercial Licensing

For commercial use, we offer flexible licensing options:

- **Enterprise License**: Full commercial rights for internal use
- **OEM License**: Integration into commercial products
- **API License**: Commercial API service deployment
- **Custom Training**: Specialized model training services

**Contact**: christian@chiliadresearch.com for pricing and terms.

## 📝 Citation

If you use HarpoonNet 1.2 in your research, please cite:

```bibtex
@misc{harpoonnet2025,
  title={HarpoonNet 1.2: Advanced Drone Detection with ConvNeXt Architecture},
  author={Christian Khoury},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/christiankhoury05/harpoon-1-2}
}
```

## 📄 License

This model is released under a **Custom Non-Commercial License**. 

- ✅ **Non-commercial use**: Freely permitted
- ❌ **Commercial use**: Requires explicit written permission
- 📧 **Licensing**: Contact christian@chiliadresearch.com

See LICENSE file for complete terms.

## 🤝 Contributing

Contributions for non-commercial use are welcome! Please feel free to submit issues and enhancement requests.

## 📞 Contact

For questions, support, and commercial licensing:
- **Email**: christian@chiliadresearch.com
- **Website**: chiliadresearch.com
- **GitHub**: [christiankhoury05](https://github.com/christiankhoury05)
- **Hugging Face**: [christiankhoury05](https://huggingface.co/christiankhoury05)

## 🔄 Model Updates

- **v1.2**: Current version with 109k+ dataset, ConvNeXt-Small backbone
- **v1.1**: Previous version with EfficientNet-B0 backbone
- **v1.0**: Initial release