Seven image labelling guidelines for the best performing model
The human eye can identify objects in a picture. However, is it possible for you to understand what the picture depicts if you’re unable to understand it? But with technological advancements such as machine learning (ML) and artificial intelligence (AI) driving the next wave of innovation, it has become necessary for computers too to possess the ability to identify objects from an image.
Computer vision, an application of ML, gives computers this ‘vision’ or the ability to identify objects in an image as a human would. Image annotation or image labelling is crucial for such ML models. In simpler terms, image labelling is labelling every element of the image to train the ML model to associate specific pixel patterns with labels.
Image labelling tips for the best performing model
Without proper labelling, your ML model may fail to identify different elements of an image for classification, detection, or segmentation. To ensure that your image labelling for ML gives you the best-trained and performing model, here are some guidelines for you to ‘sharpen’ your image annotation and labelling process for ML:
- Use tight bounding boxes: A tight bounding box can help your ML model identify which pixels are relevant and can enhance your model’s accuracy. Often, it’s challenging to differentiate between the object and the background if the picture is zoomed out. So, zoom in to get a tight bounding box while labelling your images. However, be careful not to make the box too tight to eliminate the possibility of cutting off a portion of the object of interest.
- Label occluded objects: Your image may have occluded (or partially blocked) objects. Make sure you label the occluded objects of interest for better accuracy of your computer vision model. Your bounding boxes may overlap in case of multiple occluded objects. But this should not cause an issue if the objects are accurately labelled. However, be sure to keep your image labelling objectives in mind when determining whether the occluded object is of interest or not.
- Maintain consistency: There is a degree of sensitivity in identifying virtually every object of interest. Although human vision can account for this sensitivity, maintaining high consistency during the annotation and labelling process is critical while training your ML model.
- Label everything of interest: ML models learn to correspond the pattern of pixels in an image to an object. Only labelling one appearance of the object and not various orientations can lead to false negatives as the model will fail to identify the object outside of its default orientation. Therefore, you must label every object of interest in each image to improve your model’s precision.
- Label object of interest in their entirety: Make sure your bounding box (or polygon) covers the entire object of interest. Labelling only a portion of the object is an easy way to confuse your ML model. Moreover, failure to label all the objects, across all categories, in the image can hamper the overall learning effectiveness of your ML model.
- Have clear labelling instructions: It is crucial to have a set of clear labelling instructions to ensure consistency across your annotation and labelling operations. Such clear and shareable instructions can help your team create and maintain a high-quality database, streamline the annotation process, and improve your model in the future.
- Use specific label names: It is advisable to use hyper-specific label names while labelling images for ML, which can simplify the relabelling process. With generalised rather than hyper-specific labelling, you will need a more detailed and tedious relabelling for the entire dataset.
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With Infosys BPM Annotation services for AI and ML, you can leverage the combined prowess of human intelligence and automation capabilities to build high-quality training datasets for your AI and ML models. A platform plus a human-in-loop service model from Infosys BPM allows you to dedicate your time and resources to building and to refine your models instead of worrying about building and refining a training dataset. With value propositions of flexible and scalable operating models and a dedicated annotator pool, Infosys BPM can help you classify, segment, and label images for your machine learning models.