COCO - Common Objects in Context - download
http://cocodataset.org/#home
Object segmentation
Recognition in context
Superpixel stuff segmentation
330K images (>200K labeled)
1.5 million object instances
80 object categories
91 stuff categories
5 captions per image
250,000 people with keypoints
2. Dataset -> Download
2.1. Overview
Which dataset splits should you download? Each year's split is associated with different challenges. Specifically:
For efficiently downloading the images, we recommend using gsutil rsync to avoid the download of large zip files.
http://cocodataset.org/#gsutil-rsync
Please follow the instructions in the COCO API Readme to setup the downloaded COCO data (the images and annotations). By downloading this dataset, you agree to our Terms of Use.
https://github.com/cocodataset/cocoapi
http://cocodataset.org/#termsofuse
2018 Update: coming soon! Detection and keypoint data will be unchanged, please use the 2017 data.
2017 Update: The main change in 2017 is that instead of an 80K/40K train/val split, based on community feedback the split is now 115K/5K for train/val. The same exact images are used, and no new annotations for detection/keypoints are provided. However, new in 2017 are stuff annotations on 40K train images (subset of the full 115K train images from 2017) and 5K val images. Also, for testing, in 2017 the test set only has two splits (dev / challenge), instead of the four splits (dev / standard / reserve / challenge) used in previous years. Finally, new in 2017 we are releasing 120K unlabeled images from COCO that follow the same class distribution as the labeled images; this may be useful for semi-supervised learning on COCO.
Note: Annotations last updated 09/05/2017 (stuff annotations added). If you find any issues with the data please let us know!
2.2 Download
打开 迅雷精简版 -> 复制链接地址
