YOLO11N_coreml_fp16_apple_neural_engine

This model has been converted and optimized using the Aegis AI Model Conversion Tool.

Model Details

Original Model: Unknown
Format: MLPACKAGE
Task: Object Detection
Framework: Ultralytics YOLO
License: AGPL-3.0

Performance Metrics

Metric	Value
Average FPS	134.09655979953183
Inference Time	7.457312860933598 ms
Memory Usage	740.8125 MB
Target Hardware	cpu

Hardware Information

Platform: Unknown
Device: cpu
Optimization: Hardware-specific optimizations applied

Usage

Loading the Model

# 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

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: fp16
Optimization Level: standard
Hardware Target: apple_neural_engine
Conversion Date: 2025-08-19 10:18:51

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:

{
  "avg_fps": 134.09655979953183,
  "avg_inference_time_ms": 7.457312860933598,
  "cpu_usage_percent": 7.5675,
  "memory_usage_mb": 740.8125
}

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:

@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}}
}

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Support

For issues with this converted model or the conversion tool:

This model was automatically converted and uploaded by the Aegis AI Model Conversion Tool.