- 02 Jul 2025
- Print
- DarkLight
- PDF
Overview
- Updated On 02 Jul 2025
- Print
- DarkLight
- PDF
This article provides a comprehensive set of tools for labeling various types of data, supporting a wide range of AI and machine learning tasks. These studios are designed to streamline the annotation process, improve efficiency, and ensure high-quality labeled data for model training and evaluation.
Dataloop offers specialized annotation studios to handle different data modalities, ensuring flexibility and scalability across various industries and use cases.
Supports bounding boxes, polygons, key points, cuboids, polylines, and semantic segmentation.
Allows classification, tagging, and hierarchical annotation for complex labeling needs.
Enables automation features like model-assisted annotation and active learning to speed up labeling.
Focuses on precise object segmentation using polygons and pixel-wise masks.
Supports automatic segmentation models and interactive tools like smart annotation.
Ideal for tasks requiring fine-grained object differentiation in images.
Provides frame-by-frame object tracking, interpolation, and temporal segmentation.
Supports event classification and action recognition for video AI models.
Includes smart tracking and pre-labeling features to optimize efficiency.
Designed for annotating speech, music, environmental sounds, and more.
Supports audio segmentation, speaker dialyzation, and multi-channel audio labeling.
Enables transcription and classification tasks, often used for NLP models.
Supports text classification, named entity recognition (NER), relation extraction, and sentiment analysis.
Enables multi-language annotation, including token-level, sentence-level, and document-level tagging.
Integrates GenAI models for AI-assisted labeling to speed up NLP tasks.
Data Extraction: Annotators label key fields (like dates, totals, names) in PDFs such as invoices or contracts to convert unstructured content into structured data for use in applications.
Content Review: Teams highlight, comment, and collaborate directly on PDFs for tasks like legal reviews, policy updates, or editorial checks, streamlining feedback and approval workflows.
ML/NLP Training Data: Annotated PDFs serve as ground truth for training machine learning models in tasks like entity recognition, or document classification.
Supports 3D point cloud annotation with bounding boxes, segmentation, and classification.
Works with multi-sensor fusion (LiDAR + images) for autonomous vehicle applications.
Provides semi-automatic tools for annotation acceleration.
RLHF (Reinforcement Learning from Human Feedback) Annotation Studio
Designed for human preference labeling to fine-tune AI models.
Supports comparative ranking, rating scales, and open-ended feedback.
Primarily used in GenAI model training, such as LLMs and chatbots.
Geospatial data labeling with polygon and bounding box tools.
Satellite and aerial image segmentation.
Dataset in Read-Only Mode
During export, the dataset enters Read-Only Mode to prevent changes. A warning message will appear in all annotation studios if the opened item belongs to a dataset currently being exported. While locked:
Saving and modifications are disabled.
Auto-save is off to avoid errors.
Save and Status buttons are disabled.
Actions will trigger an error message.
🔄 Use the Refresh button to check the latest status. Developer or Project Owner can click Unlock to unlock the dataset if needed. Read more