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XTREME1

XTREME1 is an Apache-2.0-licensed, self-hosted platform for annotating and curating multimodal machine-learning data, including images, 3D LiDAR point clouds and sensor-fusion datasets. It includes ontology management, data-quality tools and model-result visualization, with a beta RLHF annotation tool for LLM data.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

XTREME1 — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use XTREME1 when a team wants to host its own multimodal annotation and curation workspace, particularly for image, LiDAR or sensor-fusion data, and needs configurable labeling schemas and quality-monitoring tools.

Who it is for

  • Computer-vision teams labeling and curating image datasets.
  • Teams preparing 3D LiDAR or LiDAR-camera sensor-fusion data.
  • Machine-learning teams that need configurable class and attribute ontologies.
  • Teams exploring annotation workflows for LLM feedback data.

Documented uses

  • Label images with bounding boxes, polygons, polylines or key points.
  • Annotate 3D LiDAR and 2D/3D sensor-fusion datasets, including object-detection and segmentation tasks.
  • Define hierarchical classes and attributes for training-data schemas.
  • Monitor dataset quality, find labeling errors and correct them.
  • Visualize model results for evaluation.
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 license and documents self-hosted installation with Docker Compose.

  • The core platform installation and optional built-in model containers have different hardware requirements; model containers require Linux, NVIDIA CUDA drivers and NVIDIA Container Toolkit.
  • The README identifies RLHF annotation for LLMs as beta.
  • A fresh installation initializes database services and a test dataset; installation data is stored in Docker volumes.
  • The release package starts MySQL, Redis, MinIO, backend and frontend services.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install XTREME1 with Docker Compose

Start a fresh local XTREME1 installation using the documented release package and Docker Compose workflow.

Before you start

  • AMD64 or ARM64 machine with at least 2 GB RAM and 10 GB free disk space; storage needs vary with data size.
  • Docker Desktop 4.1 or newer on desktop systems, or Docker Engine 20.10 or newer and Docker Compose Plugin 2.0 or newer on a Linux server.

Steps

  1. Download and unzip the XTREME1 release package.

  2. Open a terminal in the extracted release directory.

  3. Run the command below to start the services.

    Documented command
    docker compose up

  4. Open http://localhost:8190 in a browser; the README recommends Google Chrome.

Expected outcome

The XTREME1 web interface is available locally after the services initialize; the installation prepares a test dataset.

Guide limitations

  • This procedure creates a fresh installation; the README directs existing installations to its upgrade instructions.
  • The optional built-in model containers are not included in this basic start procedure and have separate Linux and NVIDIA requirements.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
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Sources checked
14 October 2026
Original website title
xtreme1-io/xtreme1 · Official homepage title · retained in its source language
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