In the world of artificial intelligence and machine learning, accurate data labeling is a key component (the "Data" part of the "Data" + "Compute" + "Models" = "AI" triptych). Among the multitude of data annotation platforms available on the market, LabelMe stands out as a powerful tool for the creation of complete datasets.
LabelMe is a versatile graphical image annotation application featuring features such as image annotation and interface customization. This open-source tool offers a user-friendly interface for 🔗 annotate images making it easy to create high-quality datasets. Its use of the JSON format to store annotations makes it compatible with many machine learning frameworks.
📖 This comprehensive guide will help you master LabelMe. First, we'll take a look at what LabelMe is and why it's so useful. Then we'll move on to installing and configuring the tool on your system. We'll also explore LabelMe's basic functions, showing you how to create effective annotations. In the end, you'll have all the tools you need to use LabelMe like a pro in your AI and machine learning projects!
Introducing LabelMe
What is the LabelMe application?
LabelMe is a powerful open-source image annotation tool 🔗 created by MIT's Computer Science and Artificial Intelligence Laboratory in 2008. It's a tool for building digital image datasets with annotations, freely accessible and allowing users to contribute to its library. Developed in Python with a graphical interface based on Qt, LabelMe offers a simple, user-friendly solution for annotating images for Computer Vision" use cases.
LabelMe features beyond label creation for AI
LabelMe offers a range of features to meet the varied needs of image annotation projects:
Versatile annotation
LabelMe lets you annotate images with bounding boxes, polygons, rectangles, circles, lines and points. However, although LabelMe performs well with static images, it doesn't offer 🔗 video annotation functionality powerful enough to be used for complex use cases.
Image classification
The tool offers the option of adding flags for image classification and cleaning.
Export formats
LabelMe allows annotated data to be exported in 🔗 commonly used formats such as VOC for semantic/instance segmentation and COCO for instance segmentation.
Customizable interface
The graphical interface can be customized with predefined labels, automatic registration and label validation by quality specialists.
Cross-platform compatibility
LabelMe runs on Ubuntu, macOS and Windows.
Annotation storage
Annotations are saved in JSON format, making them easy to use in various machine learning projects.
Advantages and disadvantages
LabelMe offers several advantages that make it a popular choice for image annotation:
- Extensive library: 🔗 LabelMe boasts a vast collection of annotated images, considered by some to be canonical.
- Flexibility : the tool adapts to different annotation techniques, from object detection to 🔗 semantic segmentation.
- Ease of use: its simple graphical interface makes it accessible to users of all levels.
- Open-source: LabelMe is free of charge and allows users to contribute to its development.
- Motto : LabelMe's motto is to provide a simple and effective solution for image annotation, reflecting its commitment to quality and user-friendliness.
However, LabelMe also has a few limitations:
- No video annotation : Although LabelMe is a powerful tool for static images, it offers no video annotation functionality.
- Complexity for advanced applications : For more advanced uses, please refer to the examples provided.
🧐 In summary, LabelMe proves to be a versatile and powerful tool for image annotation, offering a wide range of features suitable for a variety of artificial intelligence and machine learning projects.
Installation and configuration
System requirements
LabelMe is a lightweight, versatile image annotation tool compatible with Windows, macOS and Linux. To install it, you need 🔗 Python 3 on your system. The use of Anaconda, a package and environment manager for Python, is recommended to simplify installation and dependency management.
Installation steps
LabelMe can be installed in several ways, depending on the platform and the user's preferences.
Installation via Anaconda (recommended) :
- Creating a new environment
conda create --name=labelme python=3
- Activate environment
For Linux/macOS :
source activate labelme
or for Windows :
conda activate labelme
- Install LabelMe
pip install labelme
- Platform-specific installation :
Ubuntu :
sudo apt-get install labelme or sudo pip3 install labelme
macOS :
brew install pyqt then pip install labelme
Windows :
Use Anaconda Prompt and follow the installation steps via Anaconda
- Using stand-alone executables :
- Download the appropriate executable from the versions section on GitHub
- These executables are particularly lightweight, with the Windows version weighing in at just 62 megabytes.
Initial configuration
Once LabelMe is installed, a few configuration steps may be necessary:
- Check installation by running LabelMe from the command line: labelme
- Customizing the graphical interface :
- Define predefined labels to speed up annotation
- Configure automatic saving to avoid data loss
- Enable label validation to ensure consistent annotations
- Familiarize yourself with :
- Explore different annotation tools: polygons, rectangles, circles, lines and points
- Test annotation of individual images and batch processing of multiple files
- Configuring export formats :
- LabelMe lets you export annotations in 🔗 formats such as Pascal-VOC and COCO
- Configure the preferred export format for semantic or instance segmentation
💡 By following these steps, users can quickly set up LabelMe and start using it effectively for their image and video annotation projects.
How do I use LabelMe?
User interface
LabelMe offers a user-friendly graphical interface for image annotation. The tool lets you annotate images for object detection, classification and segmentation. The main interface includes a sidebar with annotation tools, an image viewing area and a file list for batch processing.
To begin with, the user can open a directory containing the images to be annotated. This enables efficient batch processing of multiple files. The file list at bottom right makes it easy to select images for annotation.
Create image annotations
LabelMe offers several versatile annotation tools:
- Polygons: Ideal for segmenting complex objects. Annotations can be shared with customers to obtain feedback and improve data quality.
- Rectangles: Perfect for bounding boxes
- Circles: Useful for circular objects
- Lines: For annotating linear contours
- Points : To mark specific points of interest
To create an annotation :
- Click on "Create Polygons" in the sidebar
- Select "Edit" from the command bar to choose the annotation type
- Click on image to define annotation points
- Close the shape by clicking on the starting point
For bounding boxes, click and drag the cursor to draw the rectangle.
After creating an annotation, the user is prompted to select a class for the object. New classes can be added as and when required, and existing classes can be reused.
Backup and export
LabelMe saves annotations in JSON format, making them easy to use in various machine learning projects. To save an annotation :
- Press Command + S (macOS) or Control + S (Windows/Linux)
- The JSON file will be saved in the same folder as the annotated image
The tool also lets you export annotations in popular formats:
- Pascal-VOC format for semantic and instance segmentation
- COCO format for instance segmentation
These annotations can be exported and used in projects in Germany, where LabelMe is also very popular.
These export formats are compatible with many machine learning frameworks, making LabelMe particularly useful for computer vision projects.
Conclusion
LabelMe is proving to be an essential tool for image annotation in the field of artificial intelligence and machine learning. Its flexibility, ease of use and compatibility with various formats make it a preferred choice for computer vision projects. LabelMe's intuitive interface and versatile features enable users to create accurate, high-quality annotations, essential for training high-performance AI models.
Although LabelMe offers a robust solution for annotating static images, it's important to note its limitations, notably the absence of video annotation functionality. For projects requiring more complex or specialized annotations, it may make sense to explore other tools or call on professional services. As such, Innovatiana's annotators can produce annotations for all your use cases and help you build 🔗 datasets. Ultimately, LabelMe remains a valuable tool that has a significant impact on the development of AI and machine learning projects, facilitating the creation of annotated datasets essential for training accurate and reliable models.
