PyTorch Mobile provides a runtime environment to execute state-of-the-art machine learning models on mobile devices. Latency is reduced, privacy preserved, and models can run on mobile devices anytime, anywhere.
In this blog post, we provide a quick overview of 10 currently available PyTorch Mobile powered demo apps running various state-of-the-art PyTorch 1.9 machine learning models spanning images, video, audio and text.
It’s never been easier to deploy a state-of-the-art ML model to a phone. You don’t need any domain knowledge in Machine Learning and we hope one of the below examples resonates enough with you to be the starting point for your next project.
This app demonstrates how to use PyTorch C++ libraries on iOS and Android to classify a static image with the MobileNetv2/3 model.
iOS #1 iOS #2 Android #1 Android #2
Live Image Classification
This app demonstrates how to run a quantized MobileNetV2 and Resnet18 models to classify images in real time with an iOS and Android device camera.
This app demonstrates how to use the PyTorch DeepLabV3 model to segment images. The updated app for PyTorch 1.9 also demonstrates how to create the model using the Mobile Interpreter and load the model with the LiteModuleLoader API.
Vision Transformer for Handwritten Digit Recognition
This app demonstrates how to use Facebook’s latest optimized Vision Transformer DeiT model to do image classification and handwritten digit recognition.
This app demonstrates how to convert the popular YOLOv5 model and use it on an iOS app that detects objects from pictures in your photos, taken with camera, or with live camera.
This app demonstrates how to create and use a much lighter and faster Facebook D2Go model to detect objects from pictures in your photos, taken with camera, or with live camera.
This app demonstrates how to use a pre-trained PyTorchVideo model to perform video classification on tested videos, videos from the Photos library, or even real-time videos.
Natural Language Processing
This app demonstrates how to use a pre-trained Reddit model to perform text classification.
This app demonstrates how to convert a sequence-to-sequence neural machine translation model trained with the code in the PyTorch NMT tutorial for french to english translation.
This app demonstrates how to use the DistilBERT Hugging Face transformer model to answer questions about Pytorch Mobile itself.
This app demonstrates how to convert Facebook AI’s torchaudio-powered wav2vec 2.0, one of the leading models in speech recognition to TorchScript before deploying it.
We really hope one of these demo apps stood out for you. For the full list, make sure to visit the iOS and Android demo app repos. You should also definitely check out the video An Overview of the PyTorch Mobile Demo Apps which provides both an overview of the PyTorch mobile demo apps and a deep dive into the PyTorch Video app for iOS and Android.