Popular GitHub Repositories for Ios10

YOLOv5 ? in PyTorch > ONNX > CoreML > TFLite

ncnn is a high-performance neural network inference framework optimized for the mobile platform

Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node

Speech-to-text, text-to-speech, speaker diarization, speech enhancement, and VAD using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, HarmonyOS, Raspberry Pi, RISC-V, x86_64 servers, websocket server/client, support 11 programming languages

Collection of models for Core ML

Firebase SDK for Apple App Development

Flower: A Friendly Federated AI Framework

The Swift machine learning library.

On-device Speech Recognition for Apple Silicon

??? 专注于YOLO11，YOLOv8、TYOLOv12、YOLOv10、RT-DETR、YOLOv7、YOLOv5改进模型，Support to improve backbone, neck, head, loss, IoU, NMS and other modules?

Everything we actually know about the Apple Neural Engine (ANE)

A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

Conversational AI SDK for iOS to enable text and voice conversations with actions (Swift, Objective-C)

Query your Apple Health data with natural language ? ?

llama and other large language models on iOS and MacOS offline using GGML library.

Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.

Simple project to detect objects and display 3D labels above them in AR. This serves as a basic Template for an ARKit project to use CoreML.

A NSFW (aka porn) detector with CoreML

A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. ?25 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. ? Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet