Single-object discovery

The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.

The PASCAL Visual Object Classes (VOC) 2012 dataset contains 20 object categories including vehicles, household, animals, and other: aeroplane, bicycle, boat, bus, car, motorbike, train, bottle, chair, dining table, potted plant, sofa, TV/monitor, bird, cat, cow, dog, horse, sheep, and person. Each image in this dataset has pixel-level segmentation annotations, bounding box annotations, and object class annotations. This dataset has been widely used as a benchmark for object detection, semantic segmentation, and classification tasks. The PASCAL VOC dataset is split into three subsets: 1,464 images for training, 1,449 images for validation and a private testing set. Source: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey Image Source: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/examples/images/sheep_06.jpg

The Object Discovery dataset was collected by downloading images from Internet for airplane, car and horse. It is significantly larger and thus, diverse in terms of viewpoints, texture, color etc Source: One shot Joint Colocalization & Cosegmentation Image Source: http://people.csail.mit.edu/mrub/ObjectDiscovery/