AI Studio Feature Guide

AI Studio's unique features that help solve real-life fashion challenges.

Features unique to AI Studio

Localization

Bounding boxes are automatically added to images to focus only on the areas of interest. This helps prepare the data for building classifiers.

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Multi-category detectors

To accurately identify the category that is being learnt.

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Pose estimation

Improves accuracy by focusing on the relevant body parts to classify attributes while cropping out the noise and background.

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Domain optimized

The platform enables the user to understand and optimize their dataset for improved accuracy and results.

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Designed for Fashion Users

AI studio is an AutoML platform designed from bottom up for fashion use cases. It consists of a suite of interconnected tools to help fashion users build and deploy AI models for compelling fashion problems. This no-code platform uses fashion date – images and text – to automate machine learning for fashion.

Create projects

Projects in AI Studio are inspired by real-world fashion retail processes and reflect problems. Domain experts can leverage the platform to build models to solve real-world problems effectively using fashion data and ML. The projects are simple to create and maintain. Dataset uploads are simple and intuitive. The datasets and all related meta data can be uploaded and maintained at the project level. The iterative process enables fashion users to build robust AI models.

Train Models

The users can leverage the platform to train and deploy models using their datasets with few clicks. The platform enables users to create and compare multiple models within one project. Models can be easily downloaded or archived within the project.

Analyze and Evaluate

AI Studio takes a data-centric approach to machine learning. Equipped with features such as confusion matrices, proponents and opponents, etc., users can analyze and evaluate the performance of their models. Evaluation datasets are created at the project level to set a benchmark and measure the performance of multiple models.