Building platforms for reproducible AI research

CloudCV is a young open source cloud platform started in 2013 by students and faculty from Machine Learning and Perception Lab at Virginia Tech (now at Georgia Tech) with the aim to make AI research more reproducible. At CloudCV, we are building tools that enables researchers to build, compare and share start-of-the-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.

CloudCV consists of three major platforms:

Origami is a AI-as-a-service solution that allows researchers to easily convert their deep learning models into an online service that is widely accessible to everyone without the need to setup the infrastructure, resolve the dependencies, and build a web service around the deep learning model. By lowering the barrier to entry to latest AI algorithms, we provide developers, researchers and students the ability to access any model using a simple REST API call.

Fabrik is an online collaborative platform to build, visualize and train deep learning models by a simple drag-and-drop approach. It allow researchers to collaboratively develop and debug models using a web GUI that allows importing, editing and exporting networks from widely popular frameworks like Caffe and Keras.

EvalAI is an evaluation server that will host AI challenges like Visual Question Answering, Image Captioning. In recent years, it has become increasingly difficult to compare an algorithm solving a given task with other existing approaches. These comparisons suffer from minor differences in algorithm implementation, use of non-standard dataset splits and different evaluation metrics. By providing a central leaderboard and submission interface, we make it easier for researchers to reproduce the results mentioned in the paper and perform reliable & accurate quantitative analysis.

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