Open Images Dataset V4

[bigeagle]

数据集名称与简介(Dataset Overview)

Open Images Dataset V4 包含 1.9 million 张图，其中有 600 个分类的 15.4 million 个 bounding box，是目前最大的有标注目标检测/定位数据集。

版权信息(Copyright)

The annotations are licensed by Google Inc. under CC BY 4.0 license. The images are listed as having a CC BY 2.0 license. Note: while we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself.

使用须知(Terms and Conditions)

Please cite the following papers for any publish research:

Krasin I., Duerig T., Alldrin N., Ferrari V., Abu-El-Haija S., Kuznetsova A., Rom H., Uijlings J., Popov S., Kamali S., Malloci M., Pont-Tuset J., Veit A., Belongie S., Gomes V., Gupta A., Sun C., Chechik G., Cai D., Feng Z., Narayanan D., Murphy K. OpenImages: A public dataset for large-scale multi-label and multi-class image classification, 2017. Available from https://storage.googleapis.com/openimages/web/index.html.

@article{openimages,
  title={OpenImages: A public dataset for large-scale multi-label and multi-class image classification.},
  author={Krasin, Ivan and Duerig, Tom and Alldrin, Neil and Ferrari, Vittorio and Abu-El-Haija, Sami and Kuznetsova, Alina and Rom, Hassan and Uijlings, Jasper and Popov, Stefan and Kamali, Shahab and Malloci, Matteo and Pont-Tuset, Jordi and Veit, Andreas and Belongie, Serge and Gomes, Victor and Gupta, Abhinav and Sun, Chen and Chechik, Gal and Cai, David and Feng, Zheyun and Narayanan, Dhyanesh and Murphy, Kevin},
  journal={Dataset available from https://storage.googleapis.com/openimages/web/index.html},
  year={2017}
}

其他信息(Other)

• 数据集大小(Mirror Size):
• 官网网页(Website): https://storage.googleapis.com/openimages/web/download.html

可以使用 https://github.com/dnuffer/open_images_downloader 下载。

Dear colleagues, we are happy to announce the Open Images Dataset V4. It contains 15.4M bounding-boxes for 600 classes on 1.9M images, making it the largest existing dataset with object location annotations. The boxes have been largely manually drawn by professional annotators to ensure accuracy and consistency. The images are very diverse and often contain complex scenes with several objects (8 per image on average; visualizer).

We are also introducing the Open Images Challenge at ECCV 2018, a new object detection challenge based on the V4 data. This challenge follows in the tradition of PASCAL VOC, ImageNet and COCO, but at an unprecedented scale. The challenge has two tracks:

The Object Detection track covers 500 object classes, and has a training set of 1.7M images with 12.2M bounding-box annotations. These classes cover a broader range than previous detection challenges, including new objects such as “fedora” and “snowman” (visualizer). The Visual Relationship Detection track requires detecting pairs of objects in particular relations, e.g. "woman playing guitar", "beer on table", "dog inside car". We provide annotations for 329 distinct relationship triplets, occurring a total of 374,768 over the training set (visualizer).

We hope that the very large training set will stimulate research into more sophisticated detection models that will exceed current state-of-the-art performance. Moreover, having 500 object classes will enable assessing more precisely in which situations different detectors work best. Finally, having a large set of images with many objects annotated enables to explore Visual Relationship Detection, which is a hot emerging topic with a growing sub-community.

We hope you find this interesting, and we encourage your participation in the Challenge,

Vittorio Ferrari & the whole Open Images team

[wangtz]

+1

[huiyiqun]

open_images_downloader 似乎不支持 v4？

https://github.com/dnuffer/open_images_downloader/issues/2