

Labelu
Overview :
LabelU is an open-source data labeling tool designed for efficient annotation of image, video, and audio data, aimed at improving the performance and quality of machine learning models. It supports various annotation types, including label classification, text description, and bounding box, to meet diverse labeling needs.
Target Users :
LabelU is suitable for data scientists, machine learning engineers, and researchers who require accurate labeling of large datasets to train and optimize models.
Use Cases
Image data labeling for the autonomous driving field to train vehicle recognition models.
Annotating CT scan images in medical imaging analysis to assist in disease diagnosis.
Timestamp labeling of audio data in speech recognition technology to improve accuracy in speech-to-text conversion.
Features
Supports labeling for multiple data types, including images, videos, and audio.
Provides a variety of annotation tools such as bounding boxes, points, lines, polygons, and 3D boxes.
Includes capabilities for video segmentation, classification, and time-stamping.
Enables audio segmentation, classification, and time-stamping for audio analysis.
Facilitates task creation and management, including basic configuration, data import, and labeling setup.
Allows users to track task progress and results, as well as to export the results.
How to Use
1. Visit the LabelU website and register for an account.
2. After logging in, create a new task according to your needs.
3. Perform basic configuration, such as selecting the labeling type and importing data.
4. Configure the labeling settings, including labels and attributes.
5. Begin the labeling task, using the provided tools to annotate the data.
6. Once labeling is complete, review the progress and results of the task.
7. Export the labeled results as needed for model training or other purposes.
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