DATASETS.md

Dataset preparation

COCO Dataset

• Download the coco 2017 dataset from the official website.

Dataset strcture should look like:

${GRiT_ROOT}
|-- datasets
`-- |-- coco
    |-- |-- train2017/
    |-- |-- val2017/
    |-- |-- test2017/
    |-- |-- annotations/
    |-- |-- |-- instances_train2017.json
    |-- |-- |-- instances_val2017.json
    |-- |-- |-- image_info_test-dev2017.json

VG Dataset

• Download images from official website
• Download our pre-processed annotations: train.json and test.json

Dataset strcture should look like:

${GRiT_ROOT}
|-- datasets
`-- |-- vg
    |-- |-- images/
    |-- |-- annotations/
    |-- |-- |-- train.json
    |-- |-- |-- test.json

References

Please cite the corresponding references if you use the datasets.

 @inproceedings{lin2014microsoft,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={European conference on computer vision},
  pages={740--755},
  year={2014},
  organization={Springer}
}

 @article{krishna2017visual,
  title={Visual genome: Connecting language and vision using crowdsourced dense image annotations},
  author={Krishna, Ranjay and Zhu, Yuke and Groth, Oliver and Johnson, Justin and Hata, Kenji and Kravitz, Joshua and Chen, Stephanie and Kalantidis, Yannis and Li, Li-Jia and Shamma, David A and others},
  journal={International journal of computer vision},
  volume={123},
  number={1},
  pages={32--73},
  year={2017},
  publisher={Springer}
}