Cracked

About Cracked

The startup develops an AI platform that optimizes computer vision by transforming large foundation models into smaller, task-specific models. This approach reduces resource consumption and accelerates the deployment of computer vision applications for clients.

<problem> Training and deploying computer vision models, especially for uncommon objects or specialized tasks, requires extensive labeled data and significant computational resources. Existing foundation models often underperform on these niche applications, necessitating fine-tuning or the development of custom models, which is time-consuming and expensive. </problem> <solution> Overeasy offers IRIS, an AI-powered computer vision engineer that automates the process of labeling visual data and optimizing foundation models for specific tasks. IRIS uses prompting to guide the labeling process, achieving state-of-the-art zero-shot performance on datasets like COCO and LVIS, particularly excelling in identifying uncommon objects where other models struggle. The platform transforms large foundation models into smaller, task-specific models, reducing resource consumption and accelerating the deployment of computer vision applications. </solution> <features> - AI-powered agent for labeling visual data with prompting - State-of-the-art zero-shot object detection performance - Specialization in long-tail tasks and uncommon object identification - Automated transformation of large foundation models into smaller, task-specific models - Compatibility with datasets like COCO and LVIS </features> <target_audience> The primary target audience includes computer vision engineers, AI developers, and researchers who need to quickly and efficiently label data and deploy optimized models for specialized computer vision applications. </target_audience>

What does Cracked do?

The startup develops an AI platform that optimizes computer vision by transforming large foundation models into smaller, task-specific models. This approach reduces resource consumption and accelerates the deployment of computer vision applications for clients.

Where is Cracked located?

Cracked is based in San Francisco, United States.

When was Cracked founded?

Cracked was founded in 2023.

How much funding has Cracked raised?

Cracked has raised $500.0K.

Location
San Francisco, United States
Founded
2023
Funding
$500.0K
Employees
4 employees
Investors
Y Combinator
C

Cracked

The startup develops an AI platform that optimizes computer vision by transforming large foundation models into smaller, task-specific models. This approach reduces resource consumption and accelerates the deployment of computer vision applications for clients.

San Francisco, United StatesFounded 20234200+ followers10/10 TractionRelative Traction Score based on online presence metrics compared to companies in the same age group.
Updated 18 months ago

Funding

$500.0K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Training and deploying computer vision models, especially for uncommon objects or specialized tasks, requires extensive labeled data and significant computational resources. Existing foundation models often underperform on these niche applications, necessitating fine-tuning or the development of custom models, which is time-consuming and expensive.

Solution

Overeasy offers IRIS, an AI-powered computer vision engineer that automates the process of labeling visual data and optimizing foundation models for specific tasks. IRIS uses prompting to guide the labeling process, achieving state-of-the-art zero-shot performance on datasets like COCO and LVIS, particularly excelling in identifying uncommon objects where other models struggle. The platform transforms large foundation models into smaller, task-specific models, reducing resource consumption and accelerating the deployment of computer vision applications.

Target Audience

The primary target audience includes computer vision engineers, AI developers, and researchers who need to quickly and efficiently label data and deploy optimized models for specialized computer vision applications.

Features

  • AI-powered agent for labeling visual data with prompting
  • State-of-the-art zero-shot object detection performance
  • Specialization in long-tail tasks and uncommon object identification
  • Automated transformation of large foundation models into smaller, task-specific models
  • Compatibility with datasets like COCO and LVIS
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This profile is AI-generated and may contain inaccuracies.