#Data Annotation

Explore LabelLLM, an open-source platform enhancing data annotation efficiency tailored for developers and research teams. It provides flexible configurations, supports diverse data modalities, and includes AI-assisted features for seamless integration and precision. Benefit from a comprehensive task management system with intuitive interfaces.

The ScanNet dataset offers over 2.5 million RGB-D views from 1500 scans, complete with 3D camera poses, surface reconstructions, and semantic segmentations. It is applicable in scene understanding tasks such as 3D object classification and semantic voxel labeling. The dataset is organized by RGB-D sequence, integrating scan data, camera poses, and semantic annotations. Tools like the ScanNet C++ Toolkit and BundleFusion enhance data handling and 3D modeling. Check the ScanNet Terms of Use for accessing the data.

SEED-Bench offers a structured evaluation setup for multimodal large language models with 28K expertly annotated multiple-choice questions across 34 dimensions. Encompassing both text and image generation evaluations, it includes iterations like SEED-Bench-2 and SEED-Bench-2-Plus. Designed to assess model comprehension in complex text scenarios, SEED-Bench is a valuable resource for researchers and developers looking to compare and enhance model performance. Explore datasets and engage with the leaderboard now.

Xtreme1 is an open-source platform that enhances data annotation and ontology management for machine learning, particularly in computer vision and language models. It offers AI-driven tools to improve annotation efficiency, supporting tasks like 2D/3D object detection, segmentation, and LiDAR-Camera Fusion. The platform, available with a free plan on Xtreme1 Cloud, includes features such as dataset support, pre-labeling, interactive models, and an ontology center. It facilitates model visualization and quality monitoring and is compatible with Docker and NVIDIA GPU for model support.