CloudCV began in the summer of 2013 as a research project within the Machine Learning and Perception lab at Virginia Tech (now at Georgia Tech), with the ambitious goal of making platforms to make AI research more reproducible. We’re a young community working towards enabling developers, researchers, and fellow students to build, compare and share state-of-the-art Artificial Intelligence algorithms. We believe that one shouldn’t have to be an AI expert to have access to cutting edge vision algorithms. Likewise, researchers shouldn’t have to worry about building a service around their deep learning models to showcase and share it with others.
We have participated in the past eight installments of Google Summer of Code, over the course of which our students built several excellent tools and features. If you are interested in participating as a student or mentor, scroll down to check out our projects and get involved! We are more than happy to answer any questions you may have regarding CloudCV, so feel free to reach out to us on our Slack workspace or on our mailing list.
This project will involve writing REST API’s, plotting relevant graphs and building analytics dashboards for challenge hosts and participants. The analytics will help challenge hosts view the progress of participants in their challenge – for instance, comparing the trends of the accuracy from participant submissions over the period of time. Participants will be able to visualize the performance of all of their submissions with time and their corresponding rank on the leaderboard. The final goal is to provide users with several analytics to track their progress on the platform.
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This project will focus on streamlining the newly adopted GitHub challenge creation pipeline, building API’s for fully automating challenge creation on EvalAI, adding new capabilities in EvalAI’s latest frontend for a seamless user experience, and making our backend robust and less error-prone by adding test cases for different frontend and backend components. As of now, EvalAI admin has to be in the loop for the challenge creation process with respect to scaling worker resources for prediction-based AI challenges, setting up remote evaluation for AI challenges, and most importantly setting up code-upload AI challenges on EvalAI, the goal of this project is to remove EvalAI admin out of the loop by fully automating the process.
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This project will focus on building an infrastructure to allow exposing models submitted to EvalAI as demos in order to collect adversarial data for the model. As part of the project, we will integrate Gradio with our code upload challenge pipeline to allow deploying the models as web services. Additionally, this web service will record all interactions to curate a “in-the-wild” dataset for each submission.
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This project will focus on building a robust test suite for EvalAI’s functionalities and optimizing infrastructure cost. As part of the project we will focus on making EvalAI robust and less error-prone by adding test cases for different frontend and backend component. It will involve adding unit tests for the API suite, prediction upload evaluation workers, code upload evaluation workers (on EKS) and integration tests for the end to end testing of all the components. Additionally, we will also focus on using the EvalAI monitoring setup to identify and deploy infra cost optimization features.
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Mentoring is very important to the future of CloudCV. It introduces new people to the world of open source software who will enrich our community with their ideas and talents.
Apart from technical skills, being a mentor requires your time, a clear roadmap for your project and good organization skills. If you think you would be a good fit to mentor one of our projects, do reach out to us!
Have your own idea? Add an issue to our GSoC-Ideas repository.
In case of queries, you can contact us.
Email: team@cloudcv.org