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 500000.

Location
San Francisco, United States
Founded
2023
Funding
500000
Employees
4 employees
Major Investors
Y Combinator
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Cracked

Score: 100/100
AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

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.

overeasy.sh200+
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Crunchbase
Founded 2023 – San Francisco, United States

Funding

$

Estimated Funding

$500K+

Major Investors

Y Combinator

Team (<5)

No team information available. Click "Fetch founders" to run a focused founder search.

Company Description

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.

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

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.