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Image/Video Annotation Services
We are a leading provider of image and video annotation services for AI/ML models in computer vision projects. Our services help companies train their computer vision models to better understand and recognize images and videos.
Annotation is a critical step in the process of building machine learning models, and it’s a time-consuming and often expensive process. The quality of the annotation also plays a significant role in the quality of the model, so the accuracy and consistency of the labels is important to consider.
At Cubicent, we have a team of experienced annotators who are trained to manually label images and videos to provide the necessary data for AI/ML models. Our annotation services include object detection, semantic segmentation, and landmark identification, among others.
We understand the importance of accurate data labeling and we use a combination of tools and processes to ensure that our annotations are of the highest quality. Our annotators are trained to follow strict guidelines and standards to ensure consistent results, and we have a comprehensive quality control process in place to catch and correct any errors.
We are committed to delivering fast, reliable, and accurate image and video annotation services to help our clients get the results they need. Our flexible approach allows us to work with clients in a variety of industries, from retail and transportation to healthcare and beyond.
Bounding box annotation
Bounding box annotation is a type of data labeling used in computer vision projects to specify the location of an object within an image. It involves drawing a rectangular box, or bounding box, around the object of interest and assigning a label or class to that object. The coordinates of the bounding box, along with the class label, are used to train machine learning models to recognize and locate objects within images. Bounding box annotation is commonly used in object detection tasks, where the goal is to identify the presence and location of objects within an image. The coordinates of the bounding box are typically represented by the top-left and bottom-right corners of the box, and are typically represented in terms of pixels within the image.
Polygon annotation
Polygon annotation is a type of data labeling used in computer vision projects to specify the location of an object within an image. It involves drawing a polygon, or a closed shape, around the object of interest and assigning a label or class to that object. The coordinates of the polygon, along with the class label, are used to train machine learning models to recognize and locate objects within images.
Polygon annotation is similar to bounding box annotation, but it allows for more precise and accurate object localization. Bounding boxes are typically rectangular and might not always align well with the shape of the object, whereas polygons can be adjusted to fit the shape of the object more closely. This is particularly useful in situations where the objects in the image have complex shapes or are partially occluded.
Polygon annotation is commonly used in object detection tasks, especially when the objects in the image have complex shapes or are partially occluded. The coordinates of the polygon are typically represented by a set of points that define the shape of the polygon, and are typically represented in terms of pixels within the image.
Keypoint annotation
Keypoint annotation, also known as landmark annotation, is a type of data labeling used in computer vision projects to specify the location of specific points or features within an image. It involves marking the location of keypoints, such as the corners of eyes, nose, and mouth on a face or the joints of a person, and assigning a label or class to that object. The coordinates of the keypoints, along with the class label, are used to train machine learning models to recognize and locate objects within images.
Keypoint annotation is commonly used in tasks such as human pose estimation, facial recognition, and object tracking. It allows for more precise and accurate object localization, by marking the specific points or features of an object that are relevant to the task at hand.
The data generated from keypoint annotation is represented by a set of coordinates, where each set of coordinates corresponds to a specific keypoint. These coordinates can be represented in terms of pixels within the image, or in terms of a ratio of the image’s dimensions.
Image segmentation
Image segmentation in data labeling is the process of dividing an image into multiple segments or regions, each corresponding to a different object or part of the image. Each segment is then labeled with a class or category, such as “sky,” “building,” “person,” etc.
There are two main types of image segmentation: semantic and instance segmentation.
Semantic segmentation involves dividing an image into regions, each corresponding to a single object class. For example, an image of a city street might be divided into regions labeled “sky,” “building,” “road,” and “sidewalk.”
Instance segmentation goes a step further, it not only classifies each pixel into a specific class but also distinguish unique instances of that class in an image. For example, in an image with multiple cars, instance segmentation would label each car as a unique instance of the “car” class.
The goal of image segmentation is to accurately locate and classify all objects within an image, and it is commonly used in tasks such as object detection and image understanding. The data generated from image segmentation is represented by a mask, where each pixel in the mask corresponds to a specific object class.