A list of Android demo apps built on the powerful PyTorch Mobile platform.
PyTorch Mobile is no longer actively supported. Please check out ExecuTorch, PyTorch’s all-new on-device inference library. You can learn more about ExecuTorch’s Android demo apps here.
HelloWorld is a simple image classification application that demonstrates how to use the PyTorch Android API with the latest PyTorch 1.8, MobileNet v3, and MemoryFormat.CHANNELS_LAST.
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.
D2Go demonstrates a Python script that creates the much lighter and much faster Facebook D2Go model that is powered by PyTorch 1.8, torchvision 0.9, and Detectron2 with built-in SOTA networks for mobile, and an Android app that uses it to detect objects from pictures in your photos, taken with camera, or with live camera. This demo app also shows how to use the native pre-built torchvision-ops library.
Image Segmentation demonstrates a Python script that converts the PyTorch DeepLabV3 model and an Android app that uses the model to segment images.
Object Detection demonstrates how to convert the popular YOLOv5 model and use it in an Android app that detects objects from pictures in your photos, taken with camera, or with live camera.
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 Android app to do French-English translation.
Question Answering demonstrates how to convert a powerful transformer QA model and use the model in an Android app to answer questions about PyTorch Mobile and more.
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 Android app to perform speech recognition.
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 Android 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 Android app to perform speech recognition.
Streaming Speech Recognition demonstrates how to how to use a new torchaudio pipeline to perform streaming speech recognition, powered by Java Native Call to a C++ audio processing library for the mel spectrogram transform.
TorchVideo demonstrates how to use a pre-trained video classification model, available at the newly released PyTorchVideo, on Android 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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