C

coreml

High-performance Ultralytics YOLO inference in Rust with ONNX Runtime, GPU backends, CLI, and WebGPU/WASM.

GitHub: https://github.com/ultralytics/inference

https://docs.ultralytics.com/inference

Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.

GitHub: https://github.com/ultralytics/yolov5

https://docs.ultralytics.com/yolov5/

PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.

GitHub: https://github.com/ultralytics/yolov3

https://docs.ultralytics.com/models/yolov3/

Official Ultralytics YOLO Flutter plugin for real-time inference on Android and iOS across major vision tasks.

GitHub: https://github.com/ultralytics/yolo-flutter-app

https://pub.dev/packages/ultralytics_yolo

Ultralytics YOLO iOS app and Swift package for real-time Core ML inference across major computer vision tasks.

GitHub: https://github.com/ultralytics/yolo-ios-app

https://apps.apple.com/app/ultralytics-yolo/id1452689527

Toolchain to translate the Navajo language into American English

CocoaPods specification for the Ultralytics pod, distributing the Ultralytics.xcframework client for computer vision models hosted on the Ultralytics Platform.

GitHub: https://github.com/ultralytics/ultralytics-ios-podspecs

https://cocoapods.org/pods/Ultralytics

Ultralytics iSky iOS app for real-time neural style transfer, transforming live iPhone and iPad camera video with five famous painting styles.

GitHub: https://github.com/ultralytics/iSky

https://apps.apple.com/us/app/isky/id1445737240

Edge deployment suite for RTUAV-YOLO frequency-attention drone detector (Zhang et al.). Trained on Seraphim dataset (95.63% mAP50, 0.78M params). Features NVIDIA TensorRT (445+ FPS), Apple CoreML ANE (328+ FPS), static ONNX, FastAPI, & Docker.

On-device AI clipboard manager for macOS — semantic search, auto-categorization, zero network calls.