We recommended the following dependencies.

• Python 3.7
• PyTorch 1.11.0
• Transformers (4.18.0)
• The specific required environment can be found here

We organize all data used in the experiments in the same manner as VSEinf:

data
├── coco
│   ├── precomp  # pre-computed BUTD region features for COCO, provided by SCAN
│   │      ├── train_ids.txt
│   │      ├── train_caps.txt
│   │      ├── ......
│   │
│   ├── images   # raw coco images
│   │      ├── train2014
│   │      └── val2014
│   │
│   └── id_mapping.json  # mapping from coco-id to image's file name
│   
│
├── f30k
│   ├── precomp  # pre-computed BUTD region features for Flickr30K, provided by SCAN
│   │      ├── train_ids.txt
│   │      ├── train_caps.txt
│   │      ├── ......
│   │
│   ├── flickr30k-images   # raw coco images
│   │      ├── xxx.jpg
│   │      └── ...
│   └── id_mapping.json  # mapping from f30k index to image's file name
│
│
└── vocab  # vocab files provided by SCAN (only used when the text backbone is BiGRU)

The download links for original COCO/F30K images, precomputed BUTD features, and corresponding vocabularies are from the offical repo of SCAN. The precomp folders contain pre-computed BUTD region features, data/coco/images contains raw MS-COCO images, and data/f30k/flickr30k-images contains raw Flickr30K images.

(Update: It seems that the download link for the pre-computed features in SCAN's repo is down, this Dropbox link provides a copy of these files. Please download and follow the above file structures to organize the data.)

The id_mapping.json files are the mapping from image index (ie, the COCO id for COCO images) to corresponding filenames, we generated these mappings to eliminate the need of the pycocotools package.

Please download all necessary data files and organize them in the above manner, the path to the data directory will be the argument to the training script as shown below.

sh scripts/train.sh

sh scripts/eval.sh