Bounding box annotation is an essential step in the creation of datasets for machine learning particularly in fields such as Computer Vision. It is the simplest annotation for these models. However, accurate annotation of bounding boxes is essential for training AI models capable of detecting and localizing objects in images. In this article, we explore ten best practices for ensuring high-quality bounding box annotation.
1. Bounding Box: the importance of choosing the right tools
The first step to successfully annotating bounding boxes is to select the appropriate tools. There are a number of annotation platforms and software packages available, such as Labelbox, Supervisely, Encord, V7 Labs or Label Studio which offer advanced features to help you achieve precise results. To find out more, take a look at our Top 10 best-in-class data annotation platforms.
2. Develop clear, comprehensive instructions for image annotators
Before starting the annotation process process, establish clear and detailed guidelines for your annotators (or Data Labelers). These guidelines should include visual examples, specific instructions on how to draw bounding boxes, and rules for categorizing objects.
The annotation area should be clearly defined in a guide to avoid confusion, and it can be useful to refer to specific examples to standardize the annotation approach across different projects. Understanding these elements can greatly influence the effectiveness of computer vision models by providing them with well-structured, pixel-by-pixel accurate data.
3. Train Data Labelers in annotation techniques (Bounding Box, Keypoints, Segmentation, etc.).
It's essential to train your annotators in the fundamentals of bounding box annotation, as well as in the specifics of your project. Make sure they fully understand the objectives of your task and the specific rules to be followed. If you're working with a labeling service provider, make sure they have a training program for their teams, and regular follow-up.
Annotation management principles must be designed and communicated in a uniform way, to facilitate the identification and separation of different elements within the same image. Data labelers must have the same reflexes when using annotation rectangles to isolate and identify each object distinctly, to avoid too much variation in the annotated dataset, ensuring precise delimitation that takes every pixel into account.
4. Label classes correctly
If your annotation task involves classifying or categorizing objects, make sure that each bounding box is associated with the appropriate class. Use a color-coding or labeling system to distinguish the different classes (which is what most modern annotation tools allow you to do today - if not, consider revising your setup).
To ensure effective delineation, it is also essential to consider latitude and longitude (when spatially annotating satellite imagery, for example), so a tool that gives guidance to data labelers to help them be as precise as possible is preferable. The management of these coordinates must be integrated into the annotation platform for maximum precision. In addition, the width and height of the bounding boxes must be carefully adjusted to avoid any distortion that could affect the accuracy of the training data.
5. Don't neglect the annotation interface and its contrast
Your team of Data Labelers will be working on your data for hundreds or thousands of hours. If the interface is unintuitive or under-performing, this will have an impact on the quality of your data at the end of the process. And this (often) has nothing to do with the level of performance of the annotators. Think about contrast too: if you annotate invoices on a white background with 40 different labels, and each label is the same color (white or light colors), this will mislead annotators, make their work more difficult... and of course generate errors.
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6. Handling ambiguous or undocumented cases
Define guidelines for dealing with situations where the object to be annotated is partially visible, blurred or hidden by another object. Annotators need to be trained to identify and handle these cases appropriately... or simply ignore them to avoid creating false positives.
7. Avoid over-annotation
Be careful not to annotate empty areas or cover the same object with several bounding boxes, which can lead to model errors.
8. Maintain proportions
Bounding boxes must maintain correct proportions to faithfully reflect the size of the object in pixels. Avoid distorting or stretching them. They should be as close as possible to the object for precise delimitation, ensuring that every pixel inside the bounding box is relevant to the target object.
9. Management of partially hidden or poorly visible objects
Clearly mark the parts of objects that are partially hidden or obscured by other objects, with comments or indications (metadata) in your platform. This will help models understand the presence of obscuration.
10. Quality control, documentation and iteration
Set up a verification and quality control process to review annotations and identify errors or inconsistencies. Verification is critical to ensuring that your annotated data is correct and reliable.
Also keep a detailed record of each annotation family for future reference. Encourage annotators to provide feedback on challenges encountered during annotation. This iterative process can help improve data quality over the long term.
Bounding box annotation is a critical component of data preparation for machine learning. By following these ten best practices and integrating them into your annotation processes, you'll be able to produce high-quality bounding box annotations, resulting in better, more accurate machine learning models. Accurate bounding box annotation is essential for many applications, including object detection, image recognition and augmented reality.
