SuperAnnotate (web and desktop) data annotator tool with better accuracy, speed and precision in data annotations.
Annotations are extra information attached to parts of any kind of media, for better understanding that portion. In case of image annotations its labels, segmentation, localization, bounding boxes. For supervised machine learning, image annotation provides labels to objects in the image. Automatic Image Annotation is the new advancement in Computer Vision; it will automatically provide metadata related to the images. It is mainly used for image retrieval(searching through large databases for showing results of exact images for that text). This is better than content-based image retrieval(CBIR) process, which is query-based and requires more time to execute.
Most image annotation tools go by bounding box methodology which is the rectangle encompassing the object and giving four coordinates(left top corner, right top corner, left down right down corner) that are used by the algorithms to locate the exact object in the image. Most object detection algorithms(R-CNN, YOLO, Faster-RCnn, etc.) are built along with this methodology. But there are certain limitations to bounding box methodology – often objects are intruded by noise in the bounding box and thus fail to detect accurately by the detection algorithm. Another limitation is that apart from objects to be identified other objects are also to be annotated in some instances such as for self-driving cars, apart from cars there’s pedestrians, poles, signals and many more.
These limitations led to the development of better data annotator tools which can provide better accuracy, speed and precision in data annotations and thus today we’ll be discussing SuperAnnotate.
Cogito offers image annotation outsourcing services for machine and deep learning. One of the best image labeling companies to annotate data at best pricing.
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