PyTorch iOS examples
A list of iOS apps built on the powerful PyTorch Mobile platform.
HelloWorld is a simple image classification application that demonstrates how to use PyTorch C++ libraries on iOS. The code is written in Swift and uses Objective-C as a bridge.
HelloWorld-Metal is a simple image classification application that demonstrates how to use PyTorch C++ libraries with Metal support on iOS GPU. The code is written in Swift and uses Objective-C as a bridge.
The PyTorch demo app is a full-fledged app that contains two showcases. A camera app that runs a quantized model to classifiy images in real time. And a text-based app that uses a text classification model to predict the topic from the input text.
Neural Machine Translation demonstrates how to convert a sequence-to-sequence neural machine translation model trained with the code in the PyTorch NMT tutorial and use the model in an iOS app to do French-English translation.
Question Answering demonstrates how to convert a powerful transformer QA model and use the model in an iOS app to answer questions about PyTorch Mobile and more.
Vision Transformer demonstrates how to use Facebook's latest Vision Transformer DeiT model to do image classification, and how convert another Vision Transformer model and use it in an iOS app to perform handwritten digit recognition.
Speech Recognition demonstrates how to convert Facebook AI's wav2vec 2.0, one of the leading models in speech recognition, to TorchScript and how to use the scripted model in an iOS app to perform speech recognition.
TorchVideo demonstrates how to use a pre-trained video classification model, available at the newly released PyTorchVideo, on iOS to see video classification results, updated per second while the video plays, on tested videos, videos from the Photos library, or even real-time videos.
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