COCO 2017 Instance Segmentation.

The Microsoft COCO 2017 Instance Segmentation dataset (COCO 2017) is a large-scale benchmark for object detection and instance segmentation. It provides images with per-instance segmentation annotations (polygon masks and RLE), bounding boxes, and category labels for commonly occurring object classes (the standard COCO set of 80 detection/segmentation categories). The 2017 split commonly used for benchmarking includes train2017 and val2017 (HF mirrors list ~118,287 training images and 5,000 validation images) and test splits; annotations are provided in COCO JSON format. COCO was introduced in Lin et al., "Microsoft COCO: Common Objects in Context" (arXiv:1405.0312 / ECCV 2014) and is widely used for evaluating instance segmentation, object detection, and related tasks.