MATLAB Image Labeler - Detailed Review
Image Tools
MATLAB Image Labeler - Product Overview
The MATLAB Image Labeler App
The MATLAB Image Labeler app is a powerful tool within the Image Tools category, specifically aimed at labeling ground truth data in image collections. Here’s a brief overview of its primary function, target audience, and key features:
Primary Function
The Image Labeler app is designed to help users label regions of interest (ROI) in images. This involves defining and assigning various types of labels such as axis-aligned or rotated rectangular labels, line labels, pixel labels, polygon labels, point labels, and projected cuboid labels. These labels are crucial for training and validating algorithms like object detectors, semantic segmentation networks, and image classifiers.
Target Audience
The primary users of the Image Labeler app are researchers, engineers, and developers working in the fields of computer vision, machine learning, and image processing. This includes individuals involved in creating and training AI models, particularly those who need to prepare high-quality labeled data for their projects.
Key Features
- Labeling Tools: The app offers a variety of labeling tools, including rectangular, polygon, and pixel labels, allowing users to mark specific regions of interest in images accurately.
- Automation Algorithms: Users can write, import, and use custom automation algorithms to automatically label ground truth data, which can significantly speed up the labeling process.
- Performance Evaluation: The app allows users to evaluate the performance of their label automation algorithms using a visual summary, helping in refining the labeling process.
- Project Management: Users can create individual or team-based labeling projects, facilitating collaborative work on large datasets.
- Image Support: The app supports all image file formats that can be read by the `imread` function and can handle large images by converting them into blocked images for efficient processing.
- Segment Anything Model (SAM): The app includes the Segment Anything Model (SAM) for automatic segmentation and pixel labeling, which simplifies the process of assigning labels to objects in images.
- Export Options: Labeled data can be exported as a `groundTruth` object, which is useful for system verification or training object detectors and semantic segmentation networks.
Overall, the MATLAB Image Labeler app is an essential tool for anyone involved in preparing and labeling image data for AI and computer vision applications.
MATLAB Image Labeler - User Interface and Experience
The MATLAB Image Labeler App
The MATLAB Image Labeler app is designed to provide a user-friendly and efficient interface for labeling images, particularly for training various machine learning models such as image classifiers, object detectors, and semantic segmentation networks.
Layout and Sections
The app is divided into several key sections that make it easy to manage and label images. Here are the main components:
- Image Browser: Located at the bottom of the app, this section displays all the loaded images in a carousel format. The selected image is displayed large in the labeling window.
- Visual Summary: This browser shows the distribution of Region of Interest (ROI) and scene labels for the images in the project.
- Image Labeler Tab: This tab, which is shown by default, provides options for file management, label definition, automated labeling, monitoring, and exporting labeled data. It also includes a button to view shortcuts and tutorials.
- Visualization Tab: This tab offers options to customize the visualization of your work, such as how to display ROI labels (e.g., on hover, always displayed, or never), and label opacity variation.
- ROI Labels Pane: Here, you define the ROI label definitions to use for your project. You can create axis-aligned or rotated rectangular, polyline, pixel, and polygon ROI labels.
Labeling Tools and Automation
The app provides various tools to make the labeling process intuitive and efficient:
- Labeling Tools: You can use a brush tool, among others, to paint labels directly onto the images. These tools are accessible from the top ribbon and include options that can save you considerable time.
- Automation Algorithms: To speed up the labeling process, you can use built-in automation algorithms or define custom automation functions. This feature is accessible through the “Automate Labeling” section in the app toolstrip.
Ease of Use
The Image Labeler app is designed to be user-friendly:
- Interactive Interface: The app allows you to interactively create and manage labels directly within the app. You can add new labels, edit their colors, and save your progress as you go.
- Step-by-Step Workflow: The typical workflow involves loading images, defining labels, labeling the images using various tools, saving the session, and exporting the labeled data when complete.
- Shortcuts and Tutorials: The app includes buttons to view shortcuts and tutorials, helping new users get familiar with the labeling process quickly.
Overall User Experience
The user experience is streamlined to ensure that users can focus on labeling images without unnecessary complications:
- Clear Organization: The app’s layout is well-organized, making it easy to find and use the various features and tools.
- Flexibility: Users can define labels directly within the app or from the MATLAB command line, offering flexibility in how they work.
- Saving and Exporting: The ability to save sessions and export labeled data ensures that users can pick up where they left off and use the labeled data for training models.
Overall, the MATLAB Image Labeler app is structured to be intuitive, efficient, and flexible, making it a valuable tool for anyone needing to label images for machine learning applications.
MATLAB Image Labeler - Key Features and Functionality
The MATLAB Image Labeler App
The MATLAB Image Labeler app is a powerful tool within the MATLAB environment, specifically tailored for labeling ground truth data in images, which is crucial for training and validating machine learning and computer vision algorithms. Here are the main features and how they work:
Labeling Ground Truth Data
The app allows you to label ground truth data in a collection of images by defining various types of regions of interest (ROI) labels. These include axis-aligned or rotated rectangular, polyline, pixel, polygon, point, and projected cuboid ROI labels, as well as scene labels.
Types of Labels
- ROI Labels: You can create different shapes to mark regions of interest in images, such as rectangular, polyline, pixel, and polygon labels. This helps in identifying specific objects or areas within the images.
- Scene Labels: These labels are used to categorize the entire scene or image, which is useful for tasks like image classification.
Automation Algorithms
To speed up the labeling process, the Image Labeler app supports automation algorithms. You can:
- Use Built-in Algorithms: Select from pre-defined algorithms to automatically label your images.
- Custom Automation: Define and use your own custom automation algorithms to label ground truth data. This can be particularly useful for large datasets where manual labeling would be time-consuming.
Team-Based Labeling
The app facilitates both individual and team-based labeling projects. For team projects:
- Create and Manage Projects: You can initiate and manage labeling projects that involve multiple users.
- Distribute Tasks: Tasks can be distributed across team members, enhancing the efficiency and scale of the labeling process.
- Review and Feedback: Team members can review labeled images, provide feedback, and track the progress of labeling and review tasks.
Label Export and Integration
Once the labeling is complete, you can export the labeled ground truth data in various formats:
- Export to MATLAB Workspace: The labeled data can be exported as a `groundTruth` object, which can be used for training deep-learning-based computer vision algorithms or for system verification.
- Export to Files: Labeled data can also be saved to files, such as MAT-files, which can be used in other MATLAB workflows.
Segment Anything Model (SAM)
The Image Labeler app includes the Segment Anything Model (SAM) pixel labeling tool, which allows for automatic segmentation of images and creation of pixel labels with just a few clicks. This feature requires the Image Processing Toolbox and Deep Learning Toolbox.
User Interface and Workflow
The app provides an intuitive interface where you can:
- Import Images: Load images from folders or `imageDatastore` objects.
- Define Label Definitions: Create and manage label definitions directly within the app or from the MATLAB command line.
- Label Images: Interactively label images using various drawing tools.
- Evaluate Performance: Use visual summaries to evaluate the performance of your label automation algorithms.
These features make the MATLAB Image Labeler a versatile and efficient tool for preparing high-quality training data for machine learning and computer vision applications.
MATLAB Image Labeler - Performance and Accuracy
The MATLAB Image Labeler
The MATLAB Image Labeler is a powerful tool within the MATLAB environment, specifically designed to facilitate the labeling of ground truth data for various AI and machine learning applications. Here’s an evaluation of its performance, accuracy, and some of its limitations:
Performance
The Image Labeler app is highly efficient in handling image labeling tasks, both for individual and team-based projects. It allows users to create a variety of shapes to mark regions of interest (ROIs), including axis-aligned or rotated rectangular, polyline, pixel, and polygon labels.
To enhance performance, the app supports automation algorithms that can speed up the labeling process. Users can select from built-in automation algorithms or define and import custom algorithms. This feature significantly reduces the time required for labeling large datasets.
Accuracy
The accuracy of the Image Labeler is largely dependent on the precision of the labels created. The app provides tools to interactively specify ROIs, which are crucial for training accurate image classifiers, object detectors, and segmentation networks. The ability to manually review and adjust labels ensures high accuracy, especially when combined with automation algorithms to handle the bulk of the labeling.
Collaborative Features
For team-based projects, the Image Labeler supports a collaborative workflow, allowing multiple users to work on labeling tasks simultaneously. This feature includes the ability to review labeled images, provide feedback, and track progress, which helps maintain consistency and accuracy across the team.
Limitations
Sublabel Limitations
Sublabels, which are useful for adding detailed annotations, can only be used with certain label types (rectangle, polygon, line, and projected cuboid). Additionally, sublabels cannot have their own sublabels, and built-in automation algorithms do not support sublabel automation.
Label Summary
The Label Summary feature does not display sublabel information, which might be a limitation for projects that heavily rely on sublabels.
Automation Algorithm Limitations
While automation algorithms are powerful, they may not always capture the nuances that manual labeling can. Therefore, a combination of automated and manual labeling is often necessary to achieve high accuracy.
Areas for Improvement
Integration with Advanced Automation
While the current automation algorithms are helpful, integrating more advanced AI-driven automation tools could further streamline the labeling process and reduce manual intervention.
Enhanced Sublabel Support
Expanding the support for sublabels to include more label types and enabling automation for sublabels could enhance the app’s functionality.
Detailed Label Summaries
Including sublabel information in the Label Summary feature would provide a more comprehensive overview of the labeled data.
Overall, the MATLAB Image Labeler is a versatile and effective tool for image labeling, offering a balance of manual and automated features that enhance both performance and accuracy. However, addressing some of the limitations, particularly around sublabel support and automation, could further improve its usability and efficiency.
MATLAB Image Labeler - Pricing and Plans
To outline the pricing structure of MATLAB, including the Image Labeler app, it’s important to note that the Image Labeler app is an integral part of the MATLAB environment and does not have a standalone pricing plan. Here’s a breakdown of the relevant MATLAB pricing plans and their features:
Standard License
- This license is available for commercial, government, or other organizational use.
- It costs $2,350 USD for a perpetual license or $940 USD per year for an annual license.
Academic Use License
- Intended for use in teaching and academic research at degree-granting institutions.
- Pricing is $550 USD for a perpetual license or $275 USD per year for an annual license.
Home License
- For personal use only, not for government, academic, commercial, or other organizational use.
- Costs $95 USD per year.
Student License
- For students, this license includes MATLAB, Simulink, and several add-on products.
- The student license costs $29 USD per year for the basic version or $55 USD per year for the student suite license.
Image Labeler App
The Image Labeler app is included in the MATLAB environment and does not have a separate pricing plan. Here are some key features of the Image Labeler app:
- Ground Truth Labeling: Allows users to label images for training machine learning models.
- Semi-Automated Labeling: Tools like Fill Region, Paint by Superpixels, and Trace Boundary.
- Automated Labeling: Algorithms such as Active Contours, Adaptive Threshold, Dilate, and Erode.
- Team-Based Labeling: Enables collaborative labeling projects and management of labeling tasks.
Free Options
- MATLAB offers a free trial for its software, which includes access to the Image Labeler app. This can be a good way to test the features before committing to a purchase.
MATLAB Image Labeler - Integration and Compatibility
The MATLAB Image Labeler App
The MATLAB Image Labeler app is a versatile tool that integrates seamlessly with various other MATLAB tools and apps, particularly those involved in machine learning and computer vision workflows.
Integration with Other MATLAB Tools
1. Deep Network Designer App
The Image Labeler app can export labeled data in a format compatible with the Deep Network Designer app. You can export labels as a MATLAB struct or a pixelLabelDatastore object, which can then be imported into the Deep Network Designer app for training deep network models, such as those used in semantic segmentation.
2. Training Image Labeler App
Although the Training Image Labeler app has been replaced by the Image Labeler app, any functionality from the older app is now integrated into the Image Labeler. This ensures that you can still specify rectangular Regions of Interest (ROIs) and output training data in formats supported by functions like trainCascadeObjectDetector and vision.CascadeObjectDetector.
3. App Designer
While the Image Labeler app itself is an external tool and cannot be directly integrated into App Designer, you can use the labeled data exported from the Image Labeler in your App Designer projects. For example, you can load the exported labels into your App Designer app and use them for further processing or visualization.
Compatibility Across Platforms and Devices
1. MATLAB Environment
The Image Labeler app is fully compatible within the MATLAB environment. You can open it via the MATLAB command line by typing imageLabeler or by selecting it from the Apps tab in the MATLAB desktop. This ensures that all data and operations remain within the MATLAB ecosystem, making it easy to integrate with other MATLAB tools and scripts.
2. Data Export and Import
The app allows you to export labeled data in various formats, such as MATLAB structs or pixelLabelDatastore objects, which can be saved to files (e.g., MAT files) and imported into other MATLAB apps or scripts. This flexibility in data handling ensures compatibility across different workflows and projects.
3. Collaborative Labeling
The Image Labeler app supports collaborative, multi-user labeling workflows, which can be particularly useful in team-based projects. This feature allows multiple users to label images and review each other’s work, enhancing the efficiency and accuracy of the labeling process.
Conclusion
In summary, the MATLAB Image Labeler app is well-integrated with other MATLAB tools, particularly those related to machine learning and computer vision, and offers flexible data export and import options to ensure compatibility across various workflows and projects.
MATLAB Image Labeler - Customer Support and Resources
Support Options for MATLAB Image Labeler
For users of the MATLAB Image Labeler, several customer support options and additional resources are available to ensure a smooth and effective labeling experience.
Documentation and Guides
The primary resource for the Image Labeler is the extensive documentation provided by MathWorks. This includes detailed guides on how to open and use the Image Labeler app, create new labeling projects, and label images using various types of labels such as rectangular regions of interest (ROIs), line ROIs, pixel ROIs, and more.
Tutorials and Examples
MathWorks offers step-by-step tutorials and examples to help users get started with the Image Labeler. These tutorials cover topics like opening labeling projects, loading assigned label tasks, and manually labeling images using predefined label definitions.
Automation Algorithms
For advanced users, the Image Labeler app supports the creation and use of custom automation algorithms to speed up the labeling process. Users can write, import, and use their own automation algorithms, and the app provides templates and instructions on how to do this.
Team-Based Labeling
The Image Labeler app facilitates team-based labeling projects, allowing multiple users to work on labeling tasks collaboratively. This includes features for project owners to assign tasks and for task owners to access and complete these tasks.
Video and Community Resources
In addition to the official documentation, users can find video tutorials and community resources. For example, YouTube videos demonstrate how to use the Image Labeler in MATLAB, providing visual guidance on the labeling process.
Support from MathWorks
MathWorks provides comprehensive support through their official website, including a help section, FAQs, and contact options for further assistance. Users can also access forums and community discussions where they can ask questions and share experiences with other users.
Keyboard Shortcuts and Mouse Actions
To enhance user efficiency, the Image Labeler app includes keyboard shortcuts and mouse actions that are documented and easily accessible. This helps users to label images more quickly and accurately.
By leveraging these resources, users of the MATLAB Image Labeler can ensure they are using the tool effectively and efficiently, whether they are working on individual or team-based labeling projects.
MATLAB Image Labeler - Pros and Cons
Advantages of MATLAB Image Labeler
The MATLAB Image Labeler app offers several significant advantages, particularly in the context of AI-driven image labeling tasks:Interactive Labeling
The app provides an intuitive and interactive way to create various shapes to mark regions of interest (ROI) labels, including axis-aligned or rotated rectangles, lines, and polygon ROI labels.Handling Large Images
It allows you to label large images that do not fit into memory by converting them into blocked images, which are divided into smaller blocks that can be managed more efficiently.Automation and Efficiency
You can use automation algorithms to speed up the labeling process. This includes selecting from built-in algorithms or creating custom automation functions to label images automatically. For blocked images, you can use blocked image automation algorithms, such as automatic tumor detection in medical images.Team-Based Projects
The app supports team-based image labeling projects, enabling multiple users to collaborate on labeling tasks. It allows for reviewing labeled images, providing feedback, and tracking progress, which is crucial for large-scale projects.Export and Integration
Labeled ground truth data can be exported as a `groundTruth` object, which can be used to train deep-learning-based computer vision algorithms. This data can be exported to a MAT-file or directly to the MATLAB workspace.Visual Summary and Review
The app provides a visual summary of labeled images, allowing users to compare frames, label frequencies, and scene conditions. This feature helps in evaluating and refining the labeling process.Disadvantages of MATLAB Image Labeler
Despite its advantages, the MATLAB Image Labeler app has some limitations:Limitations with Blocked Images
When working with blocked images, pixel labeling is not supported. You can only create labels using ROI shapes such as rectangles, lines, and polygons. Additionally, images at every resolution must be registered to each other for multiresolution images.Cost and Resource Requirements
MATLAB, including the Image Labeler app, can be costly, especially when considering the need for additional toolboxes. This can be a significant barrier for individuals, students, or small organizations with limited budgets.Learning Curve
While the app has an intuitive GUI, advanced features can have a steep learning curve, requiring users to invest time in learning how to use the app effectively.Limited Pixel Labeling
For non-blocked images, while you can create pixel labels, the app’s limitations with blocked images mean that pixel labeling is not supported in such cases. In summary, the MATLAB Image Labeler app is a powerful tool for image labeling, especially in AI-driven applications, but it comes with specific limitations and costs that need to be considered. MATLAB Image Labeler - Comparison with Competitors
When comparing the MATLAB Image Labeler with other tools in the AI-driven image labeling category, several unique features and potential alternatives stand out.
Unique Features of MATLAB Image Labeler
Collaborative Labeling
The MATLAB Image Labeler app supports both individual and team-based labeling projects, allowing multiple users to collaborate on labeling tasks. This feature is particularly useful for large-scale projects where distributed teamwork is essential.
Custom Automation Algorithms
Users can write, import, and use custom automation algorithms to automatically label ground truth data. This includes integrating pretrained models from other frameworks like TensorFlow, enabling the use of a wide range of object detection and segmentation models.
Support for Various Label Types
The app allows for the creation of diverse label types, including axis-aligned or rotated rectangular regions of interest (ROI), line ROI, pixel ROI, polygon ROI, point ROI, and projected cuboid ROI labels. This versatility is beneficial for different applications such as object detection, semantic segmentation, and instance segmentation.
Integration with Medical Imaging
The Image Labeler app supports medical image formats like DICOM, NIfTI, and NRRD, making it suitable for labeling 2-D and 3-D medical image data. It also offers the option to convert large images into blocked images for efficient processing.
Built-in Detection and Tracking Algorithms
The app includes built-in algorithms for detection and tracking, such as the ACF Vehicle Detector, and supports the Segment Anything Model (SAM) for semantic segmentation tasks.
Potential Alternatives
LabelImg
LabelImg is an open-source tool for labeling object detection datasets. It is simpler and more lightweight compared to MATLAB Image Labeler but lacks the advanced features and collaborative capabilities. LabelImg is ideal for smaller projects or those requiring basic labeling functionality.
CVAT (Computer Vision Annotation Tool)
CVAT is another open-source tool that supports various annotation tasks including object detection, segmentation, and tracking. It offers collaborative features and supports multiple formats, but it may not integrate as seamlessly with deep learning frameworks as MATLAB Image Labeler does.
Hive
Hive is a cloud-based platform for data annotation that supports team collaboration and various annotation tasks. While it offers scalability and ease of use, it may lack the deep integration with MATLAB and its toolboxes that the Image Labeler provides.
Google Cloud Data Labeling
Google Cloud Data Labeling is a managed service that allows you to label data for machine learning models. It supports team collaboration and integrates well with Google Cloud AI Platform, but it might not offer the same level of customization and integration with specific toolboxes as MATLAB Image Labeler.
In summary, while other tools offer various strengths, the MATLAB Image Labeler stands out due to its comprehensive set of features, particularly its support for custom automation algorithms, collaborative labeling, and integration with medical imaging and other MATLAB toolboxes. This makes it a powerful choice for complex and large-scale image labeling projects.
MATLAB Image Labeler - Frequently Asked Questions
How do I open and use the Image Labeler app in MATLAB?
To open the Image Labeler app, you can use the MATLAB Toolstrip: go to the Apps tab, under Image Processing and Computer Vision, and click the Image Labeler app icon. Alternatively, you can enter imageLabeler in the MATLAB command prompt.
How do I assign and manage label tasks in a team-based project?
In a team-based project, the Project Owner must first assign label tasks to team members. As a Task Owner, you can open the labeling project by selecting the .prj file shared by the Project Owner and then choosing your Task Owner Name from the list. You can then select the specific task you want to perform from the Task List.
What types of labels can I create using the Image Labeler app?
The Image Labeler app allows you to create various types of labels, including Region of Interest (ROI) labels and scene labels. For example, you can label objects like lamps or video cameras using rectangle ROIs, and you can also label scenes with predefined definitions such as BustPresent.
Can I use the Image Labeler app without a full MATLAB license?
Yes, you can use the Image Labeler app without a full MATLAB license. The Project Owner can create a compiled executable file that you can use to access the shared labeling project. This executable file allows you to participate in the labeling task without needing a full MATLAB license.
How do I label images using predefined label definitions?
To label images, select the image you want to label in the Image Browser pane. Then, choose a label definition from the ROI Label Definitions pane and draw the ROI by clicking and dragging the cursor to position the rectangle around the object you want to label.
Can the Image Labeler app be used for medical image labeling?
While the Image Labeler app is general-purpose, MATLAB also offers a specific tool for medical image labeling called the Medical Image Labeler app. This app supports labeling ground truth data in medical images, including 2-D and 3-D images, and it integrates with formats like DICOM and NIfTI.
How do I include the Image Labeler in my own MATLAB application?
To include the Image Labeler in your application, you need to ensure that the Computer Vision Toolbox is included in your dependencies. You can do this by going to the Apps Tab in MATLAB, opening the Package App, and adding the necessary MathWorks products, including MATLAB and the Computer Vision Toolbox.
How can I review and provide feedback on labeled images in a team-based project?
The Image Labeler app allows you to review labeled images and provide feedback as part of a collaborative workflow. You can track the progress of labeling and review tasks, and team members can receive feedback on their labeling work. This facilitates a multi-user labeling workflow where tasks can be distributed and managed efficiently.
What file formats are supported by the Image Labeler app?
The Image Labeler app supports various image file formats. For general image labeling, it can handle most formats supported by MATLAB. For medical image labeling using the Medical Image Labeler, it supports formats like DICOM, NIfTI, NRRD, and TIFF.
Can I automate the labeling process using the Image Labeler app?
Yes, you can automate or semi-automate the labeling process. The Medical Image Labeler app, for example, allows you to use automatic algorithms like flood fill, semi-automatic techniques like interpolation, and even connect to deep learning models for segmenting radiology images. For general image labeling, you can also write and use custom automation algorithms.
How do I export labeled ground truth data from the Image Labeler app?
After labeling images, you can export the labeled ground truth data. For medical images, this data can be exported as a groundTruthMedical object, which can be used for training semantic segmentation deep learning networks or shared with colleagues.
MATLAB Image Labeler - Conclusion and Recommendation
The MATLAB Image Labeler App
The MATLAB Image Labeler app is a versatile and powerful tool within the Image Tools category, particularly for those involved in machine learning and computer vision projects. Here’s a final assessment of its capabilities and who would benefit most from using it:
Key Features
- The Image Labeler app allows users to label ground truth data in collections of images, which is crucial for training and validating AI models such as image classifiers, object detectors, and semantic and instance segmentation networks.
- It supports various types of labels, including axis-aligned and rotated rectangular, line, pixel, polygon, point, and projected cuboid ROI labels, as well as scene labels. This flexibility makes it suitable for a wide range of applications.
- The app includes automated labeling options, such as built-in detection and tracking algorithms, and the ability to write and import custom automation algorithms. This can significantly speed up the labeling process.
- The Segment Anything Model (SAM) integration allows for automatic segmentation of images and creation of pixel labels with minimal user input, which is particularly useful for semantic segmentation tasks.
Team Collaboration
- One of the standout features of the Image Labeler app is its support for team-based labeling projects. Users can create and manage projects that involve multiple team members, distribute tasks, review labeled images, provide feedback, and track progress. This collaborative workflow is essential for large-scale image labeling projects.
User Benefits
- Researchers and Developers: Those working on machine learning and computer vision projects will find the Image Labeler app invaluable for preparing high-quality training data. The app’s automation features and collaborative capabilities make it an efficient tool for both individual and team projects.
- Data Scientists: Data scientists can benefit from the app’s ability to export labeled ground truth data as `groundTruth` objects, which can be used directly for training deep-learning-based computer vision algorithms.
- Educators and Students: The app’s intuitive interface and comprehensive tutorials make it an excellent tool for educational purposes, helping students learn about image labeling and its role in AI applications.
Overall Recommendation
The MATLAB Image Labeler app is highly recommended for anyone involved in image labeling for AI and machine learning projects. Its comprehensive set of features, including support for various label types, automation algorithms, and team collaboration, make it a powerful and efficient tool. Whether you are working on individual projects or large-scale team initiatives, the Image Labeler app can significantly streamline your image labeling process and improve the quality of your ground truth data.