---
license: agpl-3.0
datasets:
- rafaelpadilla/coco2017
language:
- en
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
---
# yolo11n-coreml

This model is a CoreML-converted version of [Ultralytics/YOLO11n](https://huggingface.co/Ultralytics/YOLO11), optimized for running directly on Apple devices.

## Model Details

* **Author:** [Riddhiman Rana](https://orionlive.ai)
* **Converted from:** Ultralytics YOLOv11n PyTorch model
* **Format:** `.mlpackage` (CoreML)
* **Architecture:** YOLOv11n
* **License:** AGPL-3.0
* **Tags:** real-time, object-detection, coreml, mobile

### Compatibility

Tested on:

* iPhone 11
* iPhone 12
* iPhone 13 Pro Max
* iPhone 14
* Apple Silicon Macs: M1 & M2 Pro

Achieves real-time inference (\~30–60 FPS depending on device and resolution) in on-device vision pipelines.

## Intended Use

* Real-time object detection on iOS/macOS using CoreML
* Integration in Swift or SwiftUI apps using `VNCoreMLModel`

## Limitations

* Converted from YOLO11n: optimized for performance, not maximum accuracy
* Works best with common COCO-style classes
* Not trained or optimized for thermal/night vision, medical imaging, or domain-specific use

## How to Use

Swift code snippet to load and run the model:

```swift
import Vision
import CoreML

let model = try VNCoreMLModel(for: YOLO11n().model)
let request = VNCoreMLRequest(model: model) { request, error in
    // handle results
}
```

(Ensure `.mlpackage` is added to Xcode project.)

## Sources

* Original PyTorch model: [Ultralytics/YOLOv11](https://github.com/ultralytics/yolo)
* CoreML conversion via `coremltools`

## Citation

If you use this model, cite the original YOLO11N Model:

```bibtex
@misc{yolov11,
  author = {Ultralytics},
  title = {YOLOv11},
  year = 2024,
  url = {https://github.com/ultralytics/ultralytics}
}
```