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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.
Xtreme1 is an all-in-one data labeling and annotation platform for multimodal data training and supports 3D LiDAR point cloud, image, and LLM.
| Date | Stars |
|---|---|
| 2026-07-24 | 1224 |
| 2026-07-25 | 1225 |
| 2026-07-28 | 1225 |
| 2026-07-30 | 1225 |
| 2026-07-31 | 1225 |
| 2026-08-06 | 1225 |
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<div align="center"> <img width="386" alt="Xtreme1 logo" src="https://user-images.githubusercontent.com/84139543/190300943-98da7d5c-bd67-4074-a94f-b7405d29fb90.png">   [](https://twitter.com/Xtreme1io) [](http://docs.xtreme1.io/) </div> # Intro Xtreme1 is an all-in-one open-source platform for multimodal training data. Xtreme1 unlocks efficiency in data annotation, curation, and ontology management for tackling machine learning challenges in computer vision and LLM. The platform's AI-fueled tools elevate your annotation to the next efficiency level, powering your projects in 2D/3D Object Detection, 2D/3D Semantic/Instance Segmentation, and LiDAR-Camera Fusion like never before. Check the Enterprise Version here [🎉 Request Demo for Free](https://www.basic.ai/request-platform-demo). The README document only includes content related to installation, building, and running, if you have any questions or doubts about features, you can always refer to our [Docs Site](https://docs.xtreme1.io/xtreme1-docs/). Find us on [Twitter](https://twitter.com/Xtreme1io) | [Medium](https://medium.com/multisensory-data-training) | [Issues](https://github.com/xtreme1-io/xtreme1/issues) # Key Features Image Annotation (B-box, Segmentation) - [YOLOR](https://github.com/WongKinYiu/yolor) & [RITM](https://github.com/saic-vul/ritm_interactive_segmentation) | Lidar-camera Fusion Annotation - [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) & [AB3DMOT](https://github.com/xinshuoweng/AB3DMOT) :-------------------------:|:-------------------------:  |  :one: Supports data labeling for images, 3D LiDAR and 2D/3D Sensor Fusion datasets :two: Built-in pre-labeling and interactive models support 2D/3D object detection, segmentation and classification :three: Configurable Ontology Center for general classes (with hierarchies) and attributes for use in your model training :four: Data management and quality monitoring :five: Find labeling errors and fix them :six: Model results visualization to help you evaluate your model :seven: RLHF for Large Language Models :new: (beta version) Image Data Curation (Visualizing & Debug) - [MobileNetV3](https://github.com/xiaolai-sqlai/mobilenetv3) & [openTSNE](https://github.com/pavlin-policar/openTSNE) | RLHF Annotation Tool for LLM (beta version) :-------------------------:|:-------------------------:  | <img src="/docs/images/0.7rlhf.webp" width="640"> # Install ## Prerequisites *Operating System Requirements* Any OS can install the Xtreme1 platform with Docker Compose (installing [Docker Desktop](https://docs.docker.com/desktop/) on Mac, Windows, and Linux devices). On the Linux server, you can install Docker Engine with [Docker Compose Plugin](https://docs.docker.com/compose/install/linux/). *Hardware Requirements* **CPU**: AMD64 or ARM64 **RAM**: 2GB or higher **Hard Drive**: 10GB+ free disk space (depends on data size) *Software Requirements* For Mac, Windows, and Linux with desktop. **Docker Desktop**: 4.1 or newer For Linux server. **Docker Engine**: 20.10 or newer **Docker Compose Plugin**: 2.0 or newer *(Built-in) Models Deployment Requirements* The built-in model containers only can be running on Linux server with [NVIDIA CUDA Driver](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) and [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/index.html). **GPU**: NVIDIA T4 or other similar GPU **RAM**: 4G or higher ## Install with Docker ### Download Package Download the latest release package and unzip it. ```bash wget https://github.com/xt
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:9c272032ddc2a33a, topic:computer-vision, topic:image-classification, readme:computer vision
matched fp:9c272032ddc2a33a, topic:annotation-tool, desc:data labeling, readme:data labeling
matched fp:9c272032ddc2a33a, topic:rlhf, readme:rlhf
matched fp:9c272032ddc2a33a, topic:multimodal, desc:multimodal, readme:multimodal