data directly<\/strong> impacts the AI\/ML model’s quality. The same principle holds true for image annotations.<\/p>\n\n\n\nConsider a scenario where you’re using image annotation techniques to prepare a dataset to train an autonomous vehicle’s object recognition system. If you mislabel a motorbike in an image as a bicycle (Garbage In<\/strong>), it can misguide the model’s understanding \u2014- causing \u2014 a failure to identify a motorbike correctly, impacting its ability to navigate safely, and posing a threat to passengers, pedestrians, and other vehicles on the road (Garbage out<\/strong>). Such minor inaccuracies in image annotation can have real-world consequences.<\/p>\n\n\n\nTo avoid these pitfalls, it’s essential to invest time and effort in meticulous data labeling. This ensures that the quality of your annotations aligns with the high standards required for your machine learning model’s success. <\/p>\n\n\n\n
Here are some best practices to maintain accuracy and consistency in image datasets while annotating your images.<\/p>\n\n\n\n
Best Image Labeling Practices For Better AI\/ML Model Accuracy<\/strong><\/h2>\n\n\n\nBefore you start annotating your images, it’s essential to ensure the raw data (images) is clean. Data cleaning process purges low-quality, duplicates, and irrelevant images. Removing these unwanted elements from your dataset is fundamental for image annotation, ensuring accurate AI model training.<\/p>\n\n\n\n
Establish clear annotation guidelines<\/strong><\/h3>\n\n\n\nClear guidelines serve as a structured set of instructions for annotators, outlining precisely what aspects of an image they should annotate. For instance, if you are labeling a cat, a well-defined guideline will specify the particular focus, such as breed, color, or other relevant characteristics, that should be used to annotate the cat. This level of clarity ensures all the cats in the dataset are labeled in a uniform manner, thereby preventing disparities in how data is recorded. <\/p>\n\n\n\n
In the absence of such guidelines, there are chances that you might interpret the task differently, potentially resulting in inconsistencies, such as one annotator marking the breed on cat images while another is annotating the images by the cat\u2019s color.<\/p>\n\n\n\n
Moreover, the guidelines minimize the likelihood of personal biases or interpretations affecting the annotations. <\/p>\n\n\n\n
Label occluded objects<\/strong><\/h3>\n\n\n\nIn image annotation, occluded objects refer to elements that are partially blocked out of view in an image due to obstructions. This occurrence is common in real-world scenarios, where objects of interest might not always be entirely visible.<\/p>\n\n\n\n
A common error in annotating occluded objects is drawing partial bounding boxes around the visible portion of the object. This practice can lead to inaccuracies and hinder the model’s ability to understand the complete object.<\/p>\n\n\n\n
To maintain consistency and accuracy, it’s essential to label occluded objects as if they were fully visible. This approach ensures that the dataset contains comprehensive information about the object, even when it’s partially obstructed.<\/p>\n\n\n\n
In some cases, multiple objects of interest may appear occluded in a single image. When this happens, it’s acceptable for bounding boxes to overlap. As long as each object is accurately labeled, the overlapping boxes do not pose a problem. <\/p>\n\n\n\n
Use annotation tools with built-in validation<\/strong><\/h3>\n\n\n\nWhen annotating image datasets, consider using annotation tools that come with built-in validation features. These tools automatically check annotations for common errors and inconsistencies, such as overlapping bounding boxes or improperly closed polygons. Some best tools to consider are \u2014 Labelbox, SuperAnnotate, and LabelImg.<\/p>\n\n\n\n
Remember to evaluate these tools based on your specific project requirements, budget, and ease of integration into your workflow. Additionally, the choice of tool should align with the level of validation required to maintain consistency in your image datasets.<\/p>\n\n\n\n