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PyTorch iOS Example Apps

A list of iOS apps built on the powerful PyTorch Mobile platform.

Note

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 iOS demo apps here.

HelloWorld

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

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.

PyTorch demo app

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.

Image Segmentation

Image Segmentation demonstrates a Python script that converts the PyTorch DeepLabV3 model for mobile apps to use and an iOS app that uses the model to segment images.

Object Detection

Object Detection 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.

Neural Machine Translation

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

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

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

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.

Streaming Speech recognition

Streaming Speech Recognition demonstrates how to use the more advanced iOS AVAudioEngine to perform live audio processing and a new torchaudio pipeline to perform streaming speech recognition.

Video Classification

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.

LICENSE

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.