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--- |
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license: other |
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license_name: non-commercial |
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license_link: LICENSE |
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library_name: pytorch |
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tags: |
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- computer-vision |
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- object-detection |
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- drone-detection |
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- pytorch |
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- convnext |
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- security |
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- surveillance |
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- uav-detection |
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- aerial-vehicle |
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- defense |
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pipeline_tag: object-detection |
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model_type: object-detection |
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datasets: |
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- custom |
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language: |
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- en |
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metrics: |
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- precision |
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- recall |
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- map |
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widget: |
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- src: https://example.com/drone_sample.jpg |
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example_title: "Drone Detection Sample" |
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model-index: |
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- name: HarpoonNet 1.2 |
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results: |
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- task: |
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type: object-detection |
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name: Object Detection |
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dataset: |
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type: custom |
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name: Multi-Domain Drone Dataset |
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args: 109880 images |
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metrics: |
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- type: validation_loss |
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value: 0.059270 |
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name: Validation Loss |
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- type: parameters |
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value: 50000000 |
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name: Total Parameters |
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base_model: microsoft/convnext-small-224 |
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--- |
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# HarpoonNet 1.2 - Advanced Drone Detection Model |
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📄 **License: Non-commercial use only.** |
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🔐 **Commercial licenses available upon request. Contact: [email protected]** |
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## 🛡️ Commercial Use Notice |
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⚠️ **This model requires explicit permission for commercial use.** ⚠️ |
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- ✅ **FREE for**: Research, education, academic use, open-source projects |
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- ❌ **REQUIRES LICENSE for**: Commercial products, revenue-generating applications, proprietary systems |
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- 📧 **Contact**: [email protected] for commercial licensing |
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## Website |
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check us out @ chiliadresearch.com! |
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## Updates |
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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 |
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July 8- fixed DataParallel Issue: no more module prefix problems! |
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## 🎯 Model Description |
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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. |
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## 🏗️ Architecture |
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- **Backbone**: ConvNeXt-Small (~50M parameters) |
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- **Detection Head**: Harpoon Core (~4.7M parameters) |
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- **Total Parameters**: ~54.7M |
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- **Input Size**: 544x544 pixels |
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- **Output**: Single-class detection (drone) |
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- **Anchors**: 3 anchor boxes per grid cell |
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- **Feature Map**: 17x17 grid (544/32=17) |
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## 📊 Model Performance |
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- **Training Dataset**: 110,000+ multi-domain drone images |
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- **Validation Loss**: 0.059270 (enhanced ConvNeXt training) |
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- **Inference Speed**: ~60 FPS on modern GPU |
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- **Model Size**: ~122MB (PyTorch ConvNeXt-Small) |
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- **[email protected]**: 95%+ (superior accuracy) |
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## 🚀 Quick Start |
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### Installation |
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```bash |
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pip install torch torchvision opencv-python pillow numpy |
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``` |
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### Load Model |
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```python |
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import torch |
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from harpoon_modular import create_harpoon_net_12 |
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# Load the HarpoonNet 1.2 ConvNeXt model |
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model = create_harpoon_net_12(num_classes=1, num_anchors=3, pretrained=False) |
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checkpoint = torch.load('pytorch_model.pth', map_location='cpu') |
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# Handle both full checkpoint and weights-only files |
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if 'model_state_dict' in checkpoint: |
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model.load_state_dict(checkpoint['model_state_dict']) |
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else: |
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model.load_state_dict(checkpoint) |
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model.eval() |
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print("🚀 HarpoonNet 1.2 ConvNeXt model loaded successfully!") |
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``` |
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### Inference |
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```python |
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import cv2 |
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import torch |
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from torchvision import transforms |
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from PIL import Image |
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def preprocess_image(image_path): |
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# Load and preprocess image |
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img = cv2.imread(image_path) |
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) |
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img = cv2.resize(img, (544, 544)) # Updated resolution |
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# Convert to tensor |
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transform = transforms.Compose([ |
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transforms.ToTensor(), |
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transforms.Normalize(mean=[0.485, 0.456, 0.406], |
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std=[0.229, 0.224, 0.225]) |
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]) |
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img_tensor = transform(Image.fromarray(img)).unsqueeze(0) |
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return img_tensor |
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# Run inference |
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with torch.no_grad(): |
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img_tensor = preprocess_image('drone_image.jpg') |
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predictions = model(img_tensor) |
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detections = model.decode_predictions(predictions, confidence_threshold=0.85) # Higher threshold |
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# Process detections |
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for detection in detections[0]['boxes']: |
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print(f"Drone detected at: {detection}") |
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``` |
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## 🎮 Real-time Detection |
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The model comes with ready-to-use webcam detection scripts: |
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### Basic Detection |
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```bash |
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python webcam_detection.py --model pytorch_model.pth --conf 0.85 |
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``` |
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### Advanced Tracking |
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```bash |
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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) |
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``` |
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**Controls:** |
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- `q`: Quit |
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- `+/-`: Adjust confidence threshold |
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- `r`: Reset tracker (tracking mode) |
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- `d`: Toggle debug view |
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## 📁 Repository Contents |
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``` |
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├── pytorch_model.pth # Main model checkpoint |
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├── config.json # Model configuration |
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├── training_history.json # Training metrics and history |
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├── harpoon_modular.py # Model architecture |
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├── config_multi_dataset.py # Dataset configuration |
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├── LICENSE # Non-commercial license |
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└── README.md # This file |
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``` |
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## 🔧 Model Configuration |
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- **Classes**: 1 (drone) |
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- **Confidence Threshold**: 0.85 (recommended for high precision) |
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- **NMS Threshold**: 0.4 |
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- **Input Resolution**: 544x544 |
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- **Normalization**: ImageNet standard |
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## 📈 Training Details |
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- **Dataset Size**: 109,880+ images from 8 datasets |
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- **Training Framework**: PyTorch |
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- **Optimizer**: AdamW with cosine annealing |
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- **Learning Rate**: Enhanced warmup and decay |
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- **Augmentations**: Advanced geometric and photometric |
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- **Validation Split**: Stratified sampling |
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- **Best Epoch**: 5 (validation loss: 0.059270) |
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## 🎯 Use Cases |
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### ✅ **Permitted (Non-Commercial)** |
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- **Academic Research**: Computer vision studies and publications |
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- **Educational Projects**: University coursework and learning |
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- **Open Source Projects**: Non-profit community tools |
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- **Personal Experimentation**: Hobby and learning projects |
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### 🔐 **Requires Commercial License** |
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- **Security Systems**: Commercial perimeter monitoring |
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- **Airport Security**: Professional UAV detection systems |
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- **Military Applications**: Defense and surveillance contracts |
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- **Enterprise Software**: Proprietary detection services |
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- **API Services**: Commercial drone detection APIs |
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## ⚡ Performance Tips |
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1. **GPU Acceleration**: Use CUDA for optimal performance |
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2. **Batch Processing**: Process multiple images for efficiency |
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3. **Confidence Tuning**: Use 0.85+ for high precision applications |
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4. **Input Quality**: 544x544 resolution provides best accuracy |
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5. **Lighting**: Enhanced model performs well in various conditions |
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## 🛠️ Advanced Features |
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### ConvNeXt-Small Architecture |
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- **Modern CNN Design**: State-of-the-art computer vision backbone |
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- **Efficient Processing**: Optimized for accuracy and speed |
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- **Robust Detection**: Enhanced feature extraction capabilities |
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### ByteTrack Integration |
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- **Persistent Tracking**: Maintains object IDs across frames |
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- **Occlusion Handling**: Robust to temporary occlusions |
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- **Motion Prediction**: Kalman filter-based motion model |
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- **Track Management**: Automatic track creation and deletion |
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### Real-time Optimization |
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- **Enhanced Architecture**: Improved speed-accuracy trade-off |
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- **Memory Management**: Optimized memory footprint |
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- **Multiple Formats**: PyTorch, ONNX, TensorRT support |
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## 🏢 Commercial Licensing |
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For commercial use, we offer flexible licensing options: |
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- **Enterprise License**: Full commercial rights for internal use |
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- **OEM License**: Integration into commercial products |
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- **API License**: Commercial API service deployment |
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- **Custom Training**: Specialized model training services |
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**Contact**: [email protected] for pricing and terms. |
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## 📝 Citation |
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If you use HarpoonNet 1.2 in your research, please cite: |
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```bibtex |
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@misc{harpoonnet2025, |
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title={HarpoonNet 1.2: Advanced Drone Detection with ConvNeXt Architecture}, |
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author={Christian Khoury}, |
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year={2025}, |
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publisher={Hugging Face}, |
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url={https://huggingface.co/christiankhoury05/harpoon-1-2} |
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} |
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``` |
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## 📄 License |
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This model is released under a **Custom Non-Commercial License**. |
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- ✅ **Non-commercial use**: Freely permitted |
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- ❌ **Commercial use**: Requires explicit written permission |
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- 📧 **Licensing**: Contact [email protected] |
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See LICENSE file for complete terms. |
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## 🤝 Contributing |
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Contributions for non-commercial use are welcome! Please feel free to submit issues and enhancement requests. |
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## 📞 Contact |
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For questions, support, and commercial licensing: |
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- **Email**: [email protected] |
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- **Website**: chiliadresearch.com |
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- **GitHub**: [christiankhoury05](https://github.com/christiankhoury05) |
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- **Hugging Face**: [christiankhoury05](https://huggingface.co/christiankhoury05) |
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## 🔄 Model Updates |
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- **v1.2**: Current version with 109k+ dataset, ConvNeXt-Small backbone |
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- **v1.1**: Previous version with EfficientNet-B0 backbone |
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- **v1.0**: Initial release |
