With over seven years of experience in data annotation projects, BasicAI's data annotation platform has been trusted by leading global AI teams, contributing to the creation of more than 100,000 training datasets.
The platform is available in two versions: the open-source Xtreme1 and the enterprise version, BasicAI Data Annotation Platform.
This blog post provides a detailed comparison of the strengths and weaknesses of the two versions, as well as the types of users they are best suited for, making it easier for you to choose the right solution for your team's needs.
1. Platform Overview
1.1 Open-Source Xtreme1
Xtreme1, a project hosted by the LF DATA&AI Foundation, is an open-source platform for multi-modal training data.
It enhances the efficiency of data annotation, curation, and Ontology management to address the challenges of machine learning in computer vision and large language models (LLMs).
The platform's AI tools elevate annotation efficiency, providing support for 2D/3D object detection, and LiDAR-camera fusion projects, empowering individual AI engineers focused on small-scale model development.
➡️ Homepage: https://github.com/xtreme1-io/xtreme1
1.2 Enterprise Data Annotation Platform
BasicAI Data Annotation Platform is BasicAI's most advanced all-in-one intelligent data annotation platform for enterprise on-premises deployment.
The platform features AI-driven data annotation tools and powerful team collaboration capabilities, including smart annotation suites for image, video, point cloud, audio, and LLM data, as well as a comprehensive set of annotation project management tools.
It streamlines the entire data annotation workflow, reducing the time and resources required for annotation projects while improving data quality and expediting model development timelines.
The platform is designed for large-scale data annotation operations that require an enterprise-level scalable solution.
➡️ Homepage: https://www.basic.ai/basicai-cloud-data-annotation-platform
2. Annotation Features Comparison
In this section, we compare how Xtreme1 and BasicAI Data Annotation Platform (hereafter referred to as BasicAI Platform) perform in terms of annotation functionality.
2.1 Computer Vision Data Annotation
Both Xtreme1 and BasicAI Platform support image and point cloud data annotation. In addition, BasicAI Platform also supports video and 4D-BEV data annotation.
3D Point Cloud Object Detection & Tracking
BasicAI equips both Xtreme1 and BasicAI Platform with powerful 3D point cloud annotation capabilities. AI trainers can easily create interrelated 3D boxes and pseudo-3D boxes in 2D&3D point cloud fusion data on both platforms, providing training data for 3D perception algorithms in end devices equipped with LiDAR sensors, such as self-driving cars and robots.
On both platforms, users can run detection model inference with one click in the visualized 3D point cloud. Xtreme1 allows users to invoke their own models, while BasicAI Platform has integrated three pre-trained models optimized for autonomous driving scenarios: 3D object detection, 3D object tracking, and point cloud segmentation.
In addition to automatic annotation, smart tools on both platforms allow users to create a 3D box with just two clicks and fine-tune it using shortcuts and the three-view display. However, BasicAI Platform offers extended features:
BasicAI Platform supports LiDAR frame series annotation. A timeline at the bottom of the UI allows users to switch between frames and perform 3D object tracking annotation (automatic or manual). The continuous frame review feature enables users to inspect and fine-tune annotations of the same object across different frames in one interface.
BasicAI Platform is equipped with 3D polygon and 3D polyline annotation tools, which Xtreme1 lacks. These tools are essential for advanced intelligent driving systems, such as annotating drivable areas and lane lines.
BasicAI Platform offers multiple auxiliary tools for efficient annotation, such as missing point highlight, measuring tool, linkage pointer, and gridline.
3D Point Cloud Segmentation
3D point cloud segmentation is an exclusive feature of BasicAI Platform. Compared to object detection scenarios, segmentation finely identifies each point associated with the target.
BasicAI Platform's pre-trained point cloud segmentation model allows users to segment and label common objects such as ground, vehicles, and pedestrians in the scene with one click, greatly improving segmentation efficiency.
➡️ For more information, download the free "3D Point Cloud Annotation Guide": https://www.basic.ai/3d-lidar-point-cloud-data-annotation-guide-e-book
Image & Video Annotation
Image annotation is a common feature among annotation platforms, but BasicAI's two platforms excel in this area.
Both Xtreme1 and BasicAI Platform support bounding box, polygon, polyline, and key point annotation, meeting basic 2D perception algorithm training needs. Additionally, BasicAI Platform supports skeleton, 2D cuboid, ellipse, and mask annotation for more diverse AI algorithm scenarios.
Beyond single-image annotation, BasicAI Platform supports continuous image annotation. Users can upload continuous frames. The platform can also split a video into continuous frame images for annotation. Similar to 3D annotation, BasicAI Platform has built-in optimized models that allow users to identify and track many common objects with one click.
For video annotation, BasicAI Platform also provides video clip annotation, such as directly annotating the entry and exit points of a target on the video timeline to prepare training data for video analysis AI.
4D-BEV Fusion
Unlike 3D fusion that focuses on the visible, 4D-BEV constructs a comprehensive, top-down view of the surrounding environment across space and time, which is crucial for autonomous driving algorithms. By associating time frames, it can reveal hidden aspects, such as paths near blind spots.
BasicAI recognized the advantages of this data and introduced annotation tools for 4D-BEV fusion data in BasicAI Platform, which also supports a full set of efficient model-assisted 3D box creation for autonomous driving applications.
2.2 NLP Data Annotation
Text
Compared to Xtreme1, BasicAI Platform offers powerful and flexible text and audio annotation solutions, allowing users to customize annotation tasks according to their needs.
With its intuitive interface and comprehensive features, BasicAI enables users to efficiently create high-quality structured datasets for various natural language processing (NLP) applications.
Users can establish their own multi-level labeling system to perform fine-grained annotation of text, such as entity recognition, relation extraction, and sentiment analysis. Additionally, BasicAI supports text classification tasks, enabling users to customize classification categories according to business scenarios, such as intent recognition, topic classification, and language detection.
Audio
BasicAI Platform also provides comprehensive audio annotation capabilities. Users can upload audio files in various formats and use the platform's tools for segmentation, annotation, and transcription.
The platform supports user-defined labeling systems, allowing users to annotate audio segments with rich metadata, such as speaker identification, emotion recognition, topic classification, and language identification.
Users can also use the audio event detection feature to annotate specific events, such as keywords and noise.
Furthermore, BasicAI integrates industry-leading automatic speech recognition (ASR) engines, which can automatically convert audio into text, greatly reducing transcription time. Users can perform manual proofreading and correction based on the automatic transcription to further improve transcription quality.
Through the BasicAI platform, users can easily build large-scale, high-quality text and audio datasets, providing a reliable data foundation for training various NLP models and AI models in audio fields such as speech recognition, speaker recognition, and speech synthesis.
LLM
Both Xtreme1 and BasicAI Platform support RLHF annotation tools for large language model training.
In the RLHF stage, humans can rank and modify answers, or give instructions for the next round based on the previous round of conversation. This feedback can be viewed as a reward signal. The AI learns how to achieve the goal by finding the reward function that best explains human judgment and uses RL to gradually improve its understanding of the goal and establish a task goal model.
However, the tools on the two platforms are slightly different. In Xtreme1, users can upload conversation text data in a multi-forked tree structure, which the platform will split into individual data for annotation. Users can customize captions, such as "quality score" and "humor level," and can annotate Like/Dislike to collect user preferences. In addition, users can input detailed replies through the "long text" feature to expand dataset diversity.
Similar to the Xtreme1 platform, BasicAI Platform also provides annotation tools for RLHF tasks. Its conversation evaluation tool can label and score responses from pre-trained models based on customized criteria (such as relevance, usefulness, and safety), shaping model behavior through reinforcement learning to generate human-preferred responses.
In addition to RLHF functionality, BasicAI Platform also supports data annotation for supervised fine-tuning (SFT) tasks. Its conversation response tool facilitates the construction of SFT datasets by providing carefully designed example responses to prompts, guiding the model to give answers that meet user expectations.
Moreover, by adding contextual metadata labels such as language, domain, and level of formality to conversational data, the platform can also help enrich the model's contextual understanding capabilities, allowing it to better adapt to diverse conversational scenarios.
2.3 Note
Xtreme1 and BasicAI Platform each have their strengths in data annotation for machine vision and natural language processing. BasicAI Platform provides richer and more efficient tools and features in cutting-edge AI fields such as 3D perception, video analysis, and natural language understanding, while Xtreme1, as an open-source platform, offers high ease of use and flexibility. Users can choose the most suitable annotation platform according to their actual needs.
3. Collaboration Features
For larger AI teams, the collaboration features of the annotation platform are crucial. Smooth collaboration allows teams to identify issues and quickly complete tasks in the most suitable workflow, greatly improving the efficiency of large-scale data annotation projects.
This is a significant advantage of BasicAI Platform compared to Xtreme1, which is designed solely to conveniently solve problems for individual developers.
3.1 Annotation Workflow
BasicAI Platform's intuitive and customizable workflow system streamlines team-based data annotation, enabling users to configure scalable workflows, transparently manage internal and external teams, and ensure continuous quality improvement.
The platform's workflow tools enable users to effortlessly create and tailor annotation tasks based on the project's unique requirements. Users can split large datasets into smaller batches and flexibly allocate data among internal teams or external partners for seamless collaboration.
For both internal and external teams, project owners can meticulously manage member access and permissions through BasicAI Platform. Admins can customize access control rules for different roles, down to the granularity of viewing and annotating specific datasets, ensuring data security and privacy. The platform also provides convenient team communication tools, such as built-in chat and comment features, to facilitate collaborative communication.
For mission-critical enterprise annotation projects, BasicAI Platform's robust backend architecture provides reliable support without slowdowns or errors, even when handling massive amounts of data. The platform is optimized to deliver a completely lag-free and smooth experience, even for heavy tasks like annotating a dataset of 300 frames containing 150 million LiDAR points.
3.2 Performance System
BasicAI Platform offers a comprehensive project monitoring module that allows project owners to have real-time control over project progress and gain insights into team and individual performance.
The project dashboard presents the distribution of data at different stages through an intuitive visualization interface, along with other key task metrics such as the number of annotations created, total time spent, etc., providing data-driven support for teams to identify potential issues and optimization opportunities. Task-level dashboards track the completion status of each subtask, making it easy for managers to control the project pace.
The personnel performance panel focuses on the work performance of each annotator, recording key performance indicators such as accuracy, work duration, pass rate, and reject rate in detail. Notably, the system can also calculate accuracy with weighted adjustments based on task type and difficulty, ensuring comprehensive and fair evaluation. Managers can use this information to understand the strengths and weaknesses of team members and provide targeted performance feedback.
BasicAI Platform tailors task management interfaces for users in different roles. Annotators can view their assigned pending tasks in a centralized panel and arrange their personal work based on priorities. Quality control and review personnel can also quickly see the tasks that require their attention and work efficiently.
3.3 Quality Control System
BasicAI Platform ensures annotation quality through a multi-stage collaborative quality control mechanism.
During the annotation stage, team managers can establish automatic quality control rules for specific projects. The platform checks annotation results in real-time based on predefined rules, intelligently identifies potential errors, and prevents their submission, ensuring high accuracy of annotations at the source. The flexible customization of rules ensures a perfect match between quality control standards and project requirements.
Subsequently, quality inspectors conduct manual review of the annotation results. Annotations that do not meet quality requirements are sent back for re-annotation, further improving annotation accuracy. For bulk submissions of massive annotation results, BasicAI Platform also supports algorithm-based automatic checks, comprehensively reviewing each annotation and eliminating the risk of omissions in manual review.
Finally, only annotations approved by acceptors can be formally completed and delivered, ensuring that the data fully meets the expectations of the business side. The multi-stage quality control checkpoints provide a solid guarantee for the high quality of AI training data.
4. Summary
Xtreme1 and BasicAI Platform offer distinct data annotation solutions for different user groups and application scenarios. Users can weigh the pros and cons of the two platforms based on factors such as their business needs, budget size, and technical capabilities, and choose the solution that best suits them.
Whether they are individual developers or enterprise-level users, they can leverage these two excellent data annotation platforms to provide high-quality training data for AI model development and deployment, driving the progress and application of artificial intelligence technology.
4.1 Xtreme1: An Open-Source Platform Offering Flexibility and Control
As an open-source platform, Xtreme1's greatest strengths are its ease of use and flexibility. The platform provides basic image, point cloud, and text data annotation functions, which can meet the basic annotation needs of individual developers and small teams.
Users can use the platform for free and extend and customize its functionality according to their needs. Xtreme1's open-source nature also gives users more control and autonomy, allowing them to fully control data security and privacy.
For users with limited budgets, small data scales, and high data security and privacy requirements, Xtreme1 is undoubtedly an excellent choice.
4.2 BasicAI Platform: The Efficient Choice for Enterprise Users
With its powerful features, high performance, and enterprise-level scalability, BasicAI Platform has become the top choice for large enterprises and professional AI teams.
The platform offers rich tools and functionalities in cutting-edge AI fields such as 3D perception, video analysis, and natural language understanding, supporting various data formats and task types, significantly improving annotation efficiency and data quality. Especially in scenarios with high requirements for 3D perception capabilities, such as autonomous driving, robotics, and intelligent security, BasicAI Platform's 3D point cloud and 4D-BEV annotation capabilities are undoubtedly industry-leading.
In addition, BasicAI Platform provides powerful team collaboration features that help enterprise users efficiently manage large-scale annotation projects. Granular permission control, flexible task allocation, real-time progress tracking, and multi-dimensional quality control together build a highly collaborative and scalable annotation workflow. BasicAI Platform's stable system architecture and excellent performance can continuously support the massive data processing needs of enterprise-level users.
For enterprise users who need to efficiently complete large-scale annotation tasks with stringent data quality requirements, BasicAI Platform can be the best choice.
