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+# PillRecogNet iOS App
+This is Part 2 of my Undergraduate Thesis Project @ UniBo: an iOS application that uses Metal Performance Shaders to run a convolutional neural network on the GPU of capable devices. The ConvNet the app will run is **PillRecogNet**, trained and fine tuned to recognize pill images, as described in [Part 1](http://github.com/matteodelv/PillRecogNet).  
+  
+The main goal of this application is to allow inference through the GPU and then save classifications thanks to Core Data.  
+The core is ```PillRecogNet.swift``` which is the file that implements the entire neural network; ```Preprocessing.metal``` is a custom Metal Shader necessary to apply mean RGB value subtraction to image values, since this preprocessing has been applied during training; ```PillLabelManager.swift```, instead, is a generic implementation used to manage class labels the net will recognize. This file uses ```pillLabels.txt``` which has to be edited accordingly to your dataset.
+
+### Usage on a custom dataset
+The application can easily be used with a custom dataset, provided that the neural network has been retrained and fine tuned as described in [Part 1](http://github.com/matteodelv/PillRecogNet). In fact, this repo won't work *AS IS*: parameters for the net must be exported before running the application.
+
+1. Clone both [Part 1](http://github.com/matteodelv/PillRecogNet) and [Part 2](http://github.com/matteodelv/PillRecogNet-iOS) repos.
+2. If you plan to use a custom dataset, follow the instructions to retrain the network; otherwise, download ```fine-tuned-model.h5``` from [Part 1 Releases](https://github.com/matteodelv/PillRecogNet/releases) page.
+3. Use the script ```weights-converter.py``` to convert and export the parameters.
+4. Open this Xcode project, add these binary files and make sure they are copied in the app bundle in Build Phases settings.
+5. Change Project settings to provide your Signing Team.
+6. If you retrained the network on a custom dataset, open ```PillLabelManager.swift``` and edit the ```classesCount``` variable to reflect the number of classes in your dataset.
+7. Open and edit ```pillLabels.txt``` to provide labels for your classes. This file MUST follow the format ```index|label```, where ```index``` is a number starting from 0 and ```label``` is the class name that will be displayed in the app.  
+**NOTE:** If you retrained the network, the correspondence between indexes and labels depends on the training; this information is printed on the terminal during fine tuning or model evaluation. Otherwise, if you just want to use the fine tuned example, these labels can be downloaded from [Releases](https://github.com/matteodelv/PillRecogNet-iOS/releases) page of this repo.
+8. Now you should be able to build and run the application.
+
+### Requirements
+* Xcode 9.0+
+* iOS 10.0+
+* A real capable[^1] device (Metal Performance Shaders won't run/work on the Simulator)
+
+
+[^1]: As described in Metal Performance Shaders documentation, to run a convolutional neural network on the GPU requires devices belonging to these categories: ```GPU Family 2 v3```, ```GPU Family 3 v2```, ```GPU Family 4 v1```, or superior, which means devices with at least the Apple A8 chip (iPhone 6/6+ or later, iPad Air 2 or later, iPad mini 4, iPod touch 6G).
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