COCO is a large-scale segmentation, captioning, and object detection dataset. This dataset compiles ninety objects such as sports balls, dogs, cats, horses, persons, cars, etc. COCO was invented to aid computer applications like semantic and instance segmentation, image classification, visual answering, and object detection.
Applications of COCO
1. Object detection
COCO trains and tests object detection models that can identify and locate multiple objects in an image and assign labels.
2. Semantic segmentation
COCO is used to semantically segment images and assign labels to each pixel. When trained on COCO, models can be categorized into different regions and given various labels like person, tree, flower, etc.
3. Instance segmentation
Instance segmentation comprises semantic segmentation and object detection. COCO trains instance and segmentation models to identify and categorize individual objects in a complete image and assign labels.
4. Captioning
COCO-trained models caption images by generating natural language descriptions of the object and the various activities in the picture.
