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test-coreml-unique-123

Object Detection
ultralytics
yolo
computer-vision
unknown
aegis-ai
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test-coreml-unique-123
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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.*