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README.md

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+---
+language: en
+license: mit
+tags:
+- computer-vision
+- object-detection
+- yolov8
+- satellite-imagery
+- remote-sensing
+- vhr-10
+- geospatial
+- equipment-detection
+datasets:
+- VHR-10
+pipeline_tag: object-detection
+---
+
+# YOLOv8n Fine-tuned on VHR-10 Remote Sensing Dataset
+
+This model is a fine-tuned YOLOv8n (nano) model trained on the NWPU VHR-10 (Very High Resolution) remote sensing dataset for detecting ground equipment and vehicles in satellite imagery.
+
+## Model Description
+
+This model demonstrates the feasibility of using YOLOv8 for detecting various pieces of ground equipment through satellite imagery, serving as a proof-of-concept for commercial applications in competitive intelligence, fleet monitoring, and automated equipment detection.
+
+### Model Details
+
+- **Model Type**: YOLOv8n (nano) - Object Detection
+- **Training Dataset**: NWPU VHR-10 Remote Sensing Dataset
+- **Model Size**: ~6MB (3M parameters)
+- **Input Resolution**: 640x640 pixels
+- **Training Duration**: 50 epochs
+- **Framework**: Ultralytics YOLOv8
+
+### Detected Classes
+
+The model can detect 10 classes of objects commonly found in satellite imagery:
+
+1. **airplane** - Aircraft on airfields and airports
+2. **ship** - Naval vessels and boats
+3. **storage_tank** - Industrial storage tanks
+4. **baseball_diamond** - Baseball fields and diamonds
+5. **tennis_court** - Tennis courts and facilities
+6. **basketball_court** - Basketball courts
+7. **ground_track_field** - Athletic tracks and fields
+8. **harbor** - Harbor facilities and ports
+9. **bridge** - Bridges and overpasses
+10. **vehicle** - Ground vehicles and equipment
+
+## Performance Metrics
+
+### Overall Performance
+- **[email protected]**: 98.0% (exceptional)
+- **[email protected]:0.95**: 68.2% (good across IoU thresholds)
+- **Overall Precision**: 94.1%
+- **Overall Recall**: 96.5%
+- **Inference Speed**: 9.9ms per image
+
+### Vehicle Detection Performance (Primary Focus)
+- **Vehicle F1 Score**: 79.2%
+- **Vehicle Precision**: 87.5%
+- **Vehicle Recall**: 81.5%
+- **Vehicle [email protected]**: 88.8%
+
+### Class-wise Performance (F1 Scores)
+1. Ground Track Field: 100.0%
+2. Airplane: 98.0%
+3. Ship: 95.8%
+4. Baseball Diamond: 94.3%
+5. Tennis Court: 91.7%
+6. Basketball Court: 90.9%
+7. Bridge: 87.0%
+8. Storage Tank: 84.2%
+9. Harbor: 81.8%
+10. Vehicle: 79.2%
+
+## Intended Use
+
+### Primary Applications
+- **Proof-of-concept** for satellite-based equipment detection
+- **Competitive intelligence** and market analysis
+- **Fleet monitoring** and logistics optimization
+- **Infrastructure inventory** management
+- **Automated lead generation** based on equipment detection
+
+### Commercial Potential
+This model demonstrates that AI can reliably detect vehicles and equipment in satellite imagery, laying the groundwork for specialized commercial applications such as:
+- Hostler detection for logistics companies
+- Construction equipment monitoring
+- Fleet tracking and analysis
+- Market research and competitive analysis
+
+## Usage
+
+### Loading the Model
+
+```python
+from ultralytics import YOLO
+
+# Load the model
+model = YOLO('best.pt')
+
+# Run inference
+results = model('satellite_image.jpg')
+
+# Process results
+for result in results:
+    boxes = result.boxes
+    for box in boxes:
+        class_id = int(box.cls)
+        confidence = float(box.conf)
+        print(f"Detected: {model.names[class_id]} (confidence: {confidence:.3f})")
+```
+
+### HuggingFace Usage
+
+```python
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+
+# Download model from HuggingFace
+model_path = hf_hub_download(
+    repo_id="omgbobbyg/satellite-equipment-detection-yolov8n-vhr10",
+    filename="best.pt"
+)
+
+# Load and use model
+model = YOLO(model_path)
+results = model('your_satellite_image.jpg')
+```
+
+## Training Details
+
+### Dataset
+- **NWPU VHR-10 Dataset**: 800 very high-resolution remote sensing images
+- **Training Split**: 70% (559 images)
+- **Validation Split**: 20% (160 images)  
+- **Test Split**: 10% (81 images)
+- **Image Sources**: Google Earth and Vaihingen dataset
+
+### Training Configuration
+- **Model**: YOLOv8n (nano)
+- **Epochs**: 50
+- **Batch Size**: 8 (memory optimized)
+- **Image Size**: 640x640
+- **Optimizer**: AdamW (auto-selected)
+- **Learning Rate**: 0.000714 (auto-selected)
+- **GPU**: NVIDIA RTX 4090
+
+## Limitations and Considerations
+
+### Strengths
+- Excellent overall detection performance (98% [email protected])
+- High recall rate ensures minimal missed detections
+- Fast inference suitable for real-time applications
+- Good generalization across different object types
+
+### Limitations
+- Vehicle detection shows 49% over-prediction rate (false positives)
+- Performance varies with object size and complexity
+- Generic model - specialized training could significantly improve accuracy
+- Limited to 10 predefined classes
+
+### Recommendations for Production Use
+- Implement post-processing filtering for specific use cases
+- Consider ensemble methods for higher accuracy
+- Use larger YOLOv8 variants (s/m/l) for better precision
+- Develop specialized models for specific equipment types
+
+## Citation
+
+If you use this model in your research, please cite the original VHR-10 dataset:
+
+```bibtex
+@article{cheng2014multi,
+  title={Multi-class geospatial object detection and geographic image classification based on collection of part detectors},
+  author={Cheng, Gong and Han, Junwei and Zhou, Peicheng and Guo, Lei},
+  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
+  volume={98},
+  pages={119--132},
+  year={2014},
+  publisher={Elsevier}
+}
+```
+
+## License
+
+This model is released under the MIT License. The underlying YOLOv8 framework is licensed under GPL-3.0.