Coco Explorer – Common objects in context (cocodataset.org)

The COCO Explorer is the web gateway for the Common Objects in Context (COCO) dataset — a foundational, large-scale benchmark used across object detection, segmentation, pose estimation and image captioning research. The site centralizes downloads, per-year task datasets (Detection, Keypoints, Stuff, Panoptic, DensePose, Captions from 2015–2020), data and results formats, test/evaluation guidelines, an evaluation server, leaderboards, and source code on GitHub. COCO’s labeled corpus (hundreds of thousands of images with ~118k fully annotated images and ~1.5M object instances across 80 categories) is designed for real-world context and crowd scenes rather than isolated objects. Technically, COCO standardized evaluation practices that drove progress in deep learning: detection and segmentation use average precision (AP) across multiple IoU thresholds (commonly 0.50:0.05:0.95), keypoints use OKS-based metrics, and panoptic merges instance and semantic segmentation into a single metric. DensePose provides dense UV surface correspondences for humans. The Explorer’s formats, leaderboards and evaluation tools enable reproducible comparisons (e.g., Faster/Mask R-CNN, Panoptic FPN, transformer-based detectors). Its ubiquity has accelerated architecture and training innovations but also highlights dataset biases and generalization limits — important considerations when developing models intended for real-world deployment.