Scale AI Case Studies Scale’s Synthetic Data Enhances Kaleido AI's Visual AI Capabilities

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	Scale’s Synthetic Data Enhances Kaleido AI's Visual AI Capabilities Scale AI

Scale’s Synthetic Data Enhances Kaleido AI's Visual AI Capabilities

Scale AI

Technology Category	Analytics & Modeling - Computer Vision Software Analytics & Modeling - Machine Learning
Applicable Industries	Equipment & Machinery Plastics
Use Cases	Predictive Maintenance Visual Quality Detection
Services	Data Science Services Training
Challenge	Kaleido AI, a Vienna-based company, is dedicated to simplifying complex technology by creating tools that accelerate workflows and foster creativity. The company introduced remove.bg, an automatic image background remover, and Unscreen, a video background remover, which gained immense popularity and led to its acquisition by Canva in 2021. However, Kaleido AI faced a significant challenge in improving its machine learning models. The company's models required a large volume of high-quality data, but they encountered several edge cases in a specific segmentation task where their model performed poorly. Collecting and labeling tens of thousands of real-world images with a large diversity of patterns, images, backgrounds, and textures was difficult. Open datasets did not have enough high-quality images of this particular class. Kaleido AI initially relied on real-world data to train its segmentation models, but this approach was complex, resource-intensive, and costly. Read More
About Customer	Kaleido AI is a tech company based in Vienna, Austria, with a mission to simplify complex technology. The company creates tools that simplify and accelerate workflows, foster creativity, and enable others to bring new ideas to life. It provides access to the latest advancements in visual AI to everyone, from individuals to businesses of all sizes. In 2019, Kaleido AI introduced remove.bg, an automatic image background remover, and in 2020, it launched Unscreen, an online software that removes video backgrounds. These tools dramatically increased the speed at which users could achieve their goals, leading to a surge in their popularity. This success led to Canva acquiring Kaleido AI in early 2021. Later that year, Kaleido AI launched Designify, an AI-powered tool that creates automatic designs for various users, including individuals, car dealerships, and e-commerce websites. Read More
Solution	To overcome the challenge, Kaleido AI turned to Scale AI for help in generating synthetic data to improve their model performance on object identification and improve the Intersection over Union (IoU) of their model predictions. Scale’s machine learning engineers analyzed Kaleido AI's sample data and model inferences in Nucleus, Scale’s data curation platform. They identified that the model was performing poorly in segmenting objects in images with complex patterns, shaded or transparent objects, or where there were shadows in the backgrounds of the scenes. Scale focused on these edge cases and generated a sample of 2,650 images of synthetic data with varied lighting, textures, and patterns. However, this first pass was not sufficient to meaningfully improve model performance. The Scale team then did a deep dive into Nucleus to curate data to further identify these problem edge cases. They also introduced the ability to visualize Scale’s synthetic images compared to real images in 2D space. This analysis revealed that Scale needed to include more textured/patterned objects and a wider variety of object types in the synthetic data distribution. In total, Scale generated 14,583 synthetic images covering a total of 12 categories covering patterns, various objects, backgrounds, and textures. With this targeted synthetic data, Kaleido AI achieved an IoU of 0.794. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Computer Vision Software

Analytics & Modeling - Machine Learning

Applicable Industries

Equipment & Machinery

Plastics

Use Cases

Predictive Maintenance

Visual Quality Detection

Services

Data Science Services

Training

Challenge

Kaleido AI, a Vienna-based company, is dedicated to simplifying complex technology by creating tools that accelerate workflows and foster creativity. The company introduced remove.bg, an automatic image background remover, and Unscreen, a video background remover, which gained immense popularity and led to its acquisition by Canva in 2021. However, Kaleido AI faced a significant challenge in improving its machine learning models. The company's models required a large volume of high-quality data, but they encountered several edge cases in a specific segmentation task where their model performed poorly. Collecting and labeling tens of thousands of real-world images with a large diversity of patterns, images, backgrounds, and textures was difficult. Open datasets did not have enough high-quality images of this particular class. Kaleido AI initially relied on real-world data to train its segmentation models, but this approach was complex, resource-intensive, and costly.

About Customer

Kaleido AI is a tech company based in Vienna, Austria, with a mission to simplify complex technology. The company creates tools that simplify and accelerate workflows, foster creativity, and enable others to bring new ideas to life. It provides access to the latest advancements in visual AI to everyone, from individuals to businesses of all sizes. In 2019, Kaleido AI introduced remove.bg, an automatic image background remover, and in 2020, it launched Unscreen, an online software that removes video backgrounds. These tools dramatically increased the speed at which users could achieve their goals, leading to a surge in their popularity. This success led to Canva acquiring Kaleido AI in early 2021. Later that year, Kaleido AI launched Designify, an AI-powered tool that creates automatic designs for various users, including individuals, car dealerships, and e-commerce websites.

Solution

To overcome the challenge, Kaleido AI turned to Scale AI for help in generating synthetic data to improve their model performance on object identification and improve the Intersection over Union (IoU) of their model predictions. Scale’s machine learning engineers analyzed Kaleido AI's sample data and model inferences in Nucleus, Scale’s data curation platform. They identified that the model was performing poorly in segmenting objects in images with complex patterns, shaded or transparent objects, or where there were shadows in the backgrounds of the scenes. Scale focused on these edge cases and generated a sample of 2,650 images of synthetic data with varied lighting, textures, and patterns. However, this first pass was not sufficient to meaningfully improve model performance. The Scale team then did a deep dive into Nucleus to curate data to further identify these problem edge cases. They also introduced the ability to visualize Scale’s synthetic images compared to real images in 2D space. This analysis revealed that Scale needed to include more textured/patterned objects and a wider variety of object types in the synthetic data distribution. In total, Scale generated 14,583 synthetic images covering a total of 12 categories covering patterns, various objects, backgrounds, and textures. With this targeted synthetic data, Kaleido AI achieved an IoU of 0.794.

Impact #1	With the help of Scale Synthetic, Kaleido AI was able to improve its model performance while focusing on building their core product. The synthetic data from Scale allowed Kaleido AI to achieve continuous and efficient model performance improvement on target edge cases. Looking forward, Kaleido AI plans to continue to increase the amount of synthetic data in its dataset, using Scale’s synthetically generated data to improve its models. With Scale Synthetic, Kaleido AI has reduced the time and effort involved with synthetic data curation, allowing the team to focus on improving its core application.

Impact #1

With the help of Scale Synthetic, Kaleido AI was able to improve its model performance while focusing on building their core product. The synthetic data from Scale allowed Kaleido AI to achieve continuous and efficient model performance improvement on target edge cases. Looking forward, Kaleido AI plans to continue to increase the amount of synthetic data in its dataset, using Scale’s synthetically generated data to improve its models. With Scale Synthetic, Kaleido AI has reduced the time and effort involved with synthetic data curation, allowing the team to focus on improving its core application.

Benefit #1	Scale generated a sample of 2,650 images of synthetic data with varied lighting, textures, and patterns.
Benefit #2	Scale generated 14,583 synthetic images covering a total of 12 categories covering patterns, various objects, backgrounds, and textures.
Benefit #3	With the help of Scale's synthetic data, Kaleido AI achieved an Intersection over Union (IoU) of 0.794.

Benefit #1

Scale generated a sample of 2,650 images of synthetic data with varied lighting, textures, and patterns.

Benefit #2

Scale generated 14,583 synthetic images covering a total of 12 categories covering patterns, various objects, backgrounds, and textures.

Benefit #3

With the help of Scale's synthetic data, Kaleido AI achieved an Intersection over Union (IoU) of 0.794.

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Overview

Scale’s Synthetic Data Enhances Kaleido AI's Visual AI Capabilities

Operational Impact

Quantitative Benefit