The effectiveness of Artificial Intelligence heavily hinges on the precision of its training data. One pivotal technique for constructing training data in the realm of computer vision is image annotation. This practice is indispensable for enabling Machine Learning algorithms to comprehend objects within their surroundings, effectively teaching them to perceive the world akin to human understanding.
In the context of Machine Learning, annotation encompasses the process of assigning labels to data across diverse mediums, encompassing images, text, and video. Typically, these labels are predefined by a machine learning engineer or a computer vision expert. Their purpose is to furnish the computer vision model with insights into the objects portrayed within an image.
Subsequently, the algorithm leverages the annotated dataset to internalize and discern recurring patterns, which it can then apply when presented with novel and unprocessed data. The choice of annotation modality varies contingent on the project's nature, as different industries demand distinct forms of annotation suited to their respective requirements.
Semantic Segmentation is the task of separating an image into multiple sections and classifying every pixel in each segment to a corresponding class label of what it represents (i.e, pedestrian, car, lamp post). This gives machines a comprehensive understanding of every pixel of a scene in an image.
Semantic Segmentation is commonly used for detection and localisation of a specific object. Applications of such granular understanding of images can usually be found in a variety of industries, and it is especially popular in the Autonomous Vehicle industry, as self driving cars require deep understanding of their surroundings. While in agriculture it is used for analysis of crop fields to detect diseases and abnormal growth.
The widely utilized form of image annotation is the bounding box. This annotation method involves drawing a box around the primary objects in an image, ensuring it closely aligns with the object's edges. Bounding box annotation is used in a variety of industries for different purposes.
Polygon annotation is a more advanced image annotation technique compared to simple bounding box annotation. Polygon annotation involves outlining the object's shape using a series of interconnected points, forming a polygon that closely follows the object's contours.
Each point in the polygon represents a vertex, and these vertices collectively define the shape of the object being annotated. The use of polygons allows for more precise outlining of irregularly shaped objects and objects with complex geometries, which cannot be accurately represented by a single bounding box.
Polygon annotation is crucial for training AI and machine learning models for tasks like instance segmentation, where the model needs to distinguish between multiple instances of the same object class.
Line annotation as the name suggests involves the annotation of mainly lines and splines, which are used to draw boundaries in a region of an image. It is primarily used when a section that needs to be delineated is too small or thin and isn’t achievable by bounding box. Line annotation is commonly used to label data for autonomous vehicles.
The lines are used to train vehicle perception models for lane detection. Dissimilar to the bounding box, it avoids white space and additional noise.
Effective data annotation is an iterative process. Continuous monitoring, feedback, and improvement are key to building a high-quality labeled dataset that contributes to the success of your machine learning model.
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Everything from uploading data to seeing it labeled in real time was really cool. This is just way simpler to use compared to Amazon Sagemaker and LabelBox. I was also very impressed with how the platform delivered exactly what we needed in terms of label quality.
I was also able to view the labels as they were being generated, which gave me quick feedback about the label quality, rather than waiting for the whole batch. This replaced my standard manual QA process using external tools like Voxel's Fiftyone, as the labels were clear and easy to parse through in real-time.