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README.md

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+---
+title: YOLO Model
+tags:
+- yolo
+- object-detection
+- computer-vision
+- unknown
+- aegis-ai
+library_name: ultralytics
+license: agpl-3.0
+---
+
+# YOLO Model
+
+This model has been converted and optimized using the **Aegis AI Model Conversion Tool**.
+
+## Model Details
+
+- **Original Model**: Unknown
+- **Format**: UNKNOWN
+- **Task**: Object Detection
+- **Framework**: Ultralytics YOLO
+- **License**: AGPL-3.0
+
+## Performance Metrics
+
+| Metric | Value |
+|--------|--------|
+| Average FPS | N/A |
+| Inference Time | N/A ms |
+| Memory Usage | N/A MB |
+| Target Hardware | cpu |
+
+## Hardware Information
+
+- **Platform**: Unknown
+- **Device**: cpu
+- **Optimization**: Hardware-specific optimizations applied
+
+## Usage
+
+### Loading the Model
+
+```python
+# For ONNX models
+import onnxruntime as ort
+session = ort.InferenceSession("model.onnx")
+
+# For PyTorch models
+from ultralytics import YOLO
+model = YOLO("model.pt")
+
+# For TensorRT models (NVIDIA GPU)
+# Requires TensorRT runtime
+model = YOLO("model.engine")
+```
+
+### Inference
+
+```python
+import numpy as np
+from PIL import Image
+
+# Load your image
+image = Image.open("path/to/image.jpg")
+
+# Run inference
+results = model(image)
+
+# Process results
+for result in results:
+    boxes = result.boxes  # Bounding boxes
+    classes = result.names  # Class names
+```
+
+## Conversion Details
+
+This model was converted using the Aegis AI Model Conversion Tool with the following configuration:
+
+- **Precision**: fp32
+- **Optimization Level**: standard
+- **Hardware Target**: cpu
+- **Conversion Date**: 2025-08-18 15:11:05
+
+## Model Architecture
+
+Based on the YOLO (You Only Look Once) architecture, this model provides real-time object detection capabilities with optimized performance for the target hardware.
+
+### Input
+
+- **Shape**: 640x640
+- **Format**: RGB images
+- **Normalization**: [0-1] range
+
+### Output
+
+- **Bounding Boxes**: Object locations
+- **Confidence Scores**: Detection confidence
+- **Class Predictions**: Object categories
+
+## Benchmarking
+
+The model has been benchmarked on the target hardware with the following results:
+
+```json
+{}
+```
+
+## Hardware Compatibility
+
+This model has been optimized for:
+- **Primary**: cpu
+- **Platform**: Unknown
+
+For other hardware configurations, consider using the Aegis AI Model Conversion Tool to create optimized versions.
+
+## Citation
+
+If you use this model in your research or project, please cite:
+
+```bibtex
+@misc{aegis-ai-converted-model,
+  title={Aegis AI Converted YOLO Model},
+  author={Aegis AI Team},
+  year={2025},
+  howpublished={\url{https://github.com/aegis-ai/model-conversion-tool}}
+}
+```
+
+## Related Models
+
+- [Original YOLO Models](https://github.com/ultralytics/ultralytics)
+- [Aegis AI Model Zoo](https://huggingface.co/aegis-ai)
+
+## Support
+
+For issues with this converted model or the conversion tool:
+- [GitHub Issues](https://github.com/aegis-ai/model-conversion-tool/issues)
+- [Aegis AI Documentation](https://docs.aegis-ai.com)
+
+---
+
+*This model was automatically converted and uploaded by the Aegis AI Model Conversion Tool.*