Top-Tier Computer Vision Services

The Label Your Data team uses rectangular bounding boxes, aka 2D boxes, to carefully outline each object of interest in an image. Our goal is to provide high-quality training data that enables your CV algorithm to recognize and identify objects in images with a high degree of accuracy.

Polygons are important for computer vision tasks due to precise boundaries for objects, allowing their accurate detection and classification. Our annotators manually draw polygons around each of the target objects in your datasets. With polygons, you can also perform more complex tasks, such as multi-object detection and segmentation.

OCR annotation refers to the process of manually transcribing text from images or scanned documents into digital format. Annotators at Label Your Data can accurately identify and transcribe text from a variety of sources, allowing your OCR systems to deal with intricate handwriting, poor image quality, etc.

For this type of annotation, our annotators draw rectangular boxes around objects in three-dimensional space. They learn to comprehend different objects in the scene and their dimensions. 3D cuboids are critical for computer vision applications such as object detection, tracking, and recognition.

Key points (or landmarks) are added to an image to identify specific objects or features. We have skilled annotators who are trained to identify and label key points with accuracy and consistency. We also include a thorough QA to ensure that our annotations meet your requirements and the data’s intended use case.

Our annotators watch and analyze the video content frame by frame, and then accurately label each object, action, and event in the video. The end result is a robust video dataset that can be used to train your CV models to perform tasks such as object detection, tracking, and recognition

This is a process of assigning labels to images based on their content, which helps in organizing, retrieving, and analyzing images efficiently. We can classify images based on specific categories, such as objects, scenes, or emotions. Our expertise in various domains allows us to adapt to different client requirements.

LiDAR and RADAR annotation involves labeling objects in 3D point cloud data collected by sensors. Our annotators manually label each object, providing quality annotations for training and testing autonomous driving algorithms, as well as other CV applications that rely on this type of data.