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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
⚠️ Legacy repository for Geti v2.x. For Geti v3.0+, visit https://github.com/open-edge-platform/geti
| Date | Stars |
|---|---|
| 2026-07-24 | 484 |
| 2026-07-25 | 484 |
| 2026-07-28 | 484 |
| 2026-07-30 | 484 |
| 2026-08-06 | 484 |
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<!---
Copyright (C) 2022-2025 Intel Corporation
LIMITED EDGE SOFTWARE DISTRIBUTION LICENSE
-->
> [!IMPORTANT]
> **Geti has moved.**
>
> Active development of **Geti v3.0 and later** now takes place in the **[geti](https://github.com/open-edge-platform/geti)** repository.
>
> This repository contains the source code and history for **Geti v2.13 and earlier** only. It is no longer under active development and is maintained for historical and archival purposes.
>
> **For new features, bug fixes, documentation, and releases, please use the new repository:**
>
> 👉 **[https://github.com/open-edge-platform/geti](https://github.com/open-edge-platform/geti)**
---
<div align="center">
<p>
<a align="center" href="https://docs.geti.intel.com/">
<img
width="120%"
src="https://github.com/user-attachments/assets/9faee9f9-8c04-4287-8302-6b9d8c8675fe" />
</a>
</p>
<br>
[Key Features](#key-features) | [Documentation (v2)](https://docs.geti.intel.com/docs/2.0/user-guide/getting-started/introduction) | [License](LICENSE)
<br>
[]()
[]()
[]()
[](https://securityscorecards.dev/viewer/?uri=github.com/open-edge-platform/geti)
</div>
## 👋 Introduction
Geti™ enables anyone to build computer vision AI models in a fraction of the time and with minimal data. The software provides you with a seamless, end-to-end workflow to prepare state-of-the-art computer vision models in minutes.
<p align="center">
<img src="https://github.com/user-attachments/assets/af76a999-74d2-48bd-ae31-094e88a0d2e9" width="600"/>
</p>
Geti™ covers the complete AI model lifecycle, from dataset preparation and model training to model deployment and inferencing at the edge. You can now start building AI by running model fine-tuning on Intel® Arc™ GPU B-series. Once your computer vision solution is ready for deployment, you can easily run your OpenVINO™-optimized model across the full Intel XPU portfolio.
<p align="center">
<image src="https://github.com/user-attachments/assets/209e2aee-78cf-4d4b-aa26-a82a83f5e3ec" width="600"/>
</p>
### Key features
<details>
<summary>🏋️♂️ Interactive Model Training </summary>
Geti™ enables users to start building computer vision models with as few as 10-20 images and iterate on those models with the help of domain experts. The algorithm selects samples from the dataset that help the model learn quickly and achieve high accuracy while reducing the sample biases and the number of data inputs required from the human expert (Active learning).
<p align="center">
<img src="https://github.com/user-attachments/assets/e8d2b02a-83ba-4d05-956a-c7308f7eaf84" width="600" alt="Interactive Model Training">
</p>
</details>
<details>
<summary>🧠 Smart Annotations </summary>
Smart annotations in Geti™ enable users to easily create bounding boxes, rotated bounding boxes, segmentation boundaries, and more. These smart annotation features coupled with the AI-assisted annotations and state-of-the-art AI models such as the Segment Anything Model keep human experts in the loop while massively reducing the total annotation efforts needed by a human.
<p align="center">
<img src="https://github.com/user-attachments/assets/4cbdcd35-98b2-466e-ad24-737291cf9ab4" width="600" alt="Smart Annotations">
</p>
</details>
<details>
<summary>🤖 Visual Prompting </summary>
With Geti™ Visual Prompting workflow, users can prompt a model with only a single annotation. Utilizing <a target="_blank" rel="noopener noreferrer" href="https://arxiv.org/abs/2304.02643">the Segment Anything Model from Meta AI</a>, the Visual Prompting workflow further accelerates the time-to-modelExcerpt of 16,882 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:923c25af977e1ccf, topic:fine-tuning, readme:fine-tuning, readme:fine tuning
matched fp:923c25af977e1ccf, topic:inference
matched fp:923c25af977e1ccf, topic:deep-learning
matched fp:923c25af977e1ccf, topic:computer-vision, readme:computer vision