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Product Overview: MATLAB Image Labeler
The MATLAB Image Labeler app is a powerful tool designed to facilitate the labeling of ground truth data in image collections, a crucial step in the development of machine learning models, particularly for applications such as object detection, semantic segmentation, and instance segmentation.
Key Functionality
- Labeling Ground Truth Data: The app allows users to define and apply various types of labels to images, including axis-aligned or rotated rectangular regions of interest (ROI), line ROI, pixel ROI, polygon ROI, point ROI, projected cuboid ROI, and scene labels. This interactive labeling process enables precise annotation of images, which is essential for training accurate machine learning models.
Labeling Modes
- Individual and Team-Based Projects: Users can create either individual labeling projects or team-based projects. Team-based projects enable multiple users to collaborate on labeling tasks, distributing the workload and enhancing productivity. The app supports features for managing team roles, assigning tasks, reviewing labeled images, providing feedback, and tracking progress.
Automation and Efficiency
- Built-in Automation Algorithms: The Image Labeler app includes built-in detection and tracking algorithms to automate the labeling process. Additionally, users can write, import, and use custom automation algorithms to leverage pretrained models, such as those from TensorFlow, to accelerate the labeling process.
Data Management and Export
- Data Import and Export: The app supports all image file formats compatible with the `imread` function. Users can import images from folders or `imageDatastore` objects. Labeled ground truth data can be exported as a `groundTruth` object, which is useful for system verification or for training object detectors and semantic segmentation networks.
Collaboration and Review
- Collaborative Workflow: The app facilitates a collaborative workflow by allowing team members to work on different aspects of the labeling project simultaneously. It includes features for creating label tasks, publishing tasks, and reviewing labeled images. Team members can provide feedback and correct labels, ensuring high-quality ground truth data.
Programmatic Use
- Programmatic Access: The Image Labeler app can be opened programmatically using commands such as `imageLabeler`, `imageLabeler(imageFolder)`, or `imageLabeler(imageDatastore)`, allowing for integration into larger workflows and scripts.
Evaluation and Summary
- Performance Evaluation: Users can evaluate the performance of their label automation algorithms using a visual summary, helping to refine and improve the labeling process.
In summary, the MATLAB Image Labeler app is a comprehensive tool that streamlines the image labeling process, supports both individual and team-based workflows, and integrates well with other MATLAB tools for machine learning and computer vision tasks. Its robust features for automation, collaboration, and data management make it an indispensable asset for anyone involved in training and validating AI models.
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