Yolo11n Coreml

--- 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} } ```

Source context: 1 downloads · 0 likes · Pipeline object-detection · Repo edwsimon/yolo11n-coreml

Source context: 3 downloads · 0 likes · Pipeline object-detection · Repo edwsimon/yolo11n-coreml