Project Information

The aim of the project is to provide hands-on experience on Computational Photography topics together with MLops paragidms. Throughout the project, students are intended to have freedom on topic/backend technology/mobile platform selection.

Roadmap

• Do read papers and seek at least one github repositories involving source code of recent approaches (2019+)
• Train the model or find a pre-trained model belonging to the subject of interest
• Create a solution in which the client is an Android/iOS app which uploads a test image to a server for inferencing and retrieving the resultant image
• The server can be AWS, Azure or even your own PC and it functions as the model inference server
• The client should accept an image and send it to the model server in order to retrieve the result back!
• Performing the inference natively on Android/Apple device (i.e. No server requirement) (+30 points). However, this requires a special treatment which could be quantization and you must show and describe the work of your own.
• Inference stage should be completed under 1 minute
• You can benefit from Flask or Streamlit as the ML backend server. However, you cannot use a browser based mobile app to demonstrate your project. The mobile app must be native.
• A final report including all the key findings, architecture, run-time measurement and build procedure is a mandatory (25 points)
• Any custom optimization for the extra speedup that you do it by yourself (e.g. quantization of the trained model) will be rewarded with 10 points
• Any cheating, if detected, will be penalized with 0 points. However, you can, for sure, exhange ideas and ask help for building mobile app

Important Notes & Evaluation

• The project can be done in pairs. Whole source code for the project must include different subfolders for different phases (i.e. deep model, backend service, mobile app).
• The project must be presented by at least one student at the end of the semester. The week will be announced in the class.