Encord Computer Vision Glossary
Label in computer vision refers to a textual or numerical annotation that is assigned to an object or region of interest within an image or video. Labels are commonly used in supervised machine learning applications to train algorithms to recognize and classify objects in visual data. They can be used to identify objects, define their boundaries, or describe their attributes, such as color, shape, or texture. Labels are typically manually assigned by human annotators or generated automatically using computer vision algorithms. The quality and accuracy of labels can have a significant impact on the performance of computer vision systems.
Understanding Label Quality
The quality of labels in computer vision refers to how accurately and consistently annotations have been applied to the visual data. High-quality labels are essential for training accurate machine learning models that can identify and classify objects and features within images. The quality of labels can be affected by several factors, such as the expertise and experience of the annotators, the quality of the annotation tools used, and the complexity and ambiguity of the objects being labeled. To ensure high-quality labels, it is essential to have clear guidelines, standards, and processes in place for labeling, as well as to perform quality control checks and validations on the labels. This can help ensure that the labels are consistent, accurate, and reliable, which is crucial for the success of computer vision applications. The label quality can also be improved by using automatic labeling tools with humans in the loop.
Automated labeling, also known as automatic annotation, is a process in computer vision that uses machine learning algorithms to apply labels to visual data, such as images or videos. Automated labeling can be used to reduce the time and cost required for manual labeling and can be particularly useful for large datasets. There are several techniques for automated labeling, including object detection, semantic segmentation, and instance segmentation, which involve identifying and classifying objects within an image and labeling them accordingly. While automated labeling can be efficient, it can also be less accurate than manual labeling, particularly in cases where the visual data is complex or ambiguous. As such, a combination of automated and manual labeling is often used to ensure the highest quality of labels for training machine learning models.