# xtreme1-io/xtreme1

Xtreme1 is an all-in-one data labeling and annotation platform for multimodal data training and supports 3D LiDAR point cloud, image, and LLM.

Repository: https://github.com/xtreme1-io/xtreme1
Canonical: https://ross.abutalabs.com/products/xtreme1
Homepage: https://www.basic.ai
Language: TypeScript
License: Apache-2.0
License Family: permissive
Topics: annotation-tool, annotation, computer-vision, image-annotation, image-classification, image-labelling-tool, labeling-tool, 3d-annotation, point-cloud, rlhf, multimodal, lidar-camera-fusion, lidar-object-detection, lidar-object-tracking, lidar-annotation
Last push: 2026-06-15T13:23:33+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 87, release rhythm 8, longevity 100
- inputs: {"age_days": 1486, "days_push": 79, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1234, forks 216 (observed 2026-08-28T04:04:04.729125+00:00)

## What it is
Xtreme1 is an open-source, self-hosted data labeling and annotation platform for multimodal training data, supporting images, 3D LiDAR point clouds, LiDAR-camera fusion, and LLM RLHF workflows. It includes AI-assisted pre-labeling, an ontology center, data curation, and model result visualization, deployed via Docker Compose.

## Use cases
- annotate 3d lidar point clouds for object detection
- label images with bounding boxes and segmentation
- annotate lidar-camera fusion datasets
- run rlhf data labeling for llm training
- manage ontology classes and attributes for training data
- find and fix labeling errors in datasets
- pre-label data with ai-assisted models

## When to choose
- you need an open-source, self-hosted alternative to commercial annotation platforms
- your datasets combine 2D images and 3D LiDAR point clouds
- you want AI-assisted pre-labeling to speed up annotation
- you need RLHF annotation workflows for LLM training

## When to avoid
- you only need simple text or audio annotation without 3D support
- you want a fully managed cloud service without self-hosting
- you need a lightweight single-purpose labeling script rather than a full platform

## Facets
- artifact type: application
- maturity: active
- function: image-processing, computer-vision, machine-learning, data-science, nlp
- domain: computer-vision, machine-learning, data-science, artificial-intelligence, large-language-models
- platform: self-hosted, cross-platform
- tags: data-labeling, annotation-tool, point-cloud, lidar, 3d-annotation, rlhf, multimodal, sensor-fusion, ontology-management, pre-labeling, docker, web-server

## Member repositories
- xtreme1-io/xtreme1 (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:04.729125+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:22:12.364557+00:00, confidence not recorded.
  - readme: https://github.com/xtreme1-io/xtreme1 (fetched 2026-08-28T04:04:04.729125+00:00, sha eb8937176c21)
  - homepage: https://www.basic.ai (fetched 2026-08-29T12:21:41.131914+00:00, sha 3008f910e1f2)
- Data as of 2026-08-30T08:39:29.467469+00:00.