Create a new directory data to store all the datasets.
Download the dataset from the official website COCO.
RefCOCO/+/g use the COCO2014 train split.
Download the annotation files from github.
Convert the annotation files:
python3 tools/data/convert_refexp_to_coco.py
Finally, we expect the directory structure to be the following:
MUTR
├── data
│ ├── coco
│ │ ├── train2014
│ │ ├── refcoco
│ │ │ ├── instances_refcoco_train.json
│ │ │ ├── instances_refcoco_val.json
│ │ ├── refcoco+
│ │ │ ├── instances_refcoco+_train.json
│ │ │ ├── instances_refcoco+_val.json
│ │ ├── refcocog
│ │ │ ├── instances_refcocog_train.json
│ │ │ ├── instances_refcocog_val.json
Download the dataset from the competition's website here. Then, extract and organize the file. We expect the directory structure to be the following:
MUTR
├── data
│ ├── ref-youtube-vos
│ │ ├── meta_expressions
│ │ ├── train
│ │ │ ├── JPEGImages
│ │ │ ├── Annotations
│ │ │ ├── meta.json
│ │ ├── valid
│ │ │ ├── JPEGImages
Downlaod the DAVIS2017 dataset from the website. Note that you only need to download the two zip files DAVIS-2017-Unsupervised-trainval-480p.zip and DAVIS-2017_semantics-480p.zip.
Download the text annotations from the website.
Then, put the zip files in the directory as follows.
MUTR
├── data
│ ├── ref-davis
│ │ ├── DAVIS-2017_semantics-480p.zip
│ │ ├── DAVIS-2017-Unsupervised-trainval-480p.zip
│ │ ├── davis_text_annotations.zip
Unzip these zip files.
unzip -o davis_text_annotations.zip
unzip -o DAVIS-2017_semantics-480p.zip
unzip -o DAVIS-2017-Unsupervised-trainval-480p.zip
Preprocess the dataset to Ref-Youtube-VOS format. (Make sure you are in the main directory)
python tools/data/convert_davis_to_ytvos.py
Finally, unzip the file DAVIS-2017-Unsupervised-trainval-480p.zip again (since we use mv in preprocess for efficiency).
unzip -o DAVIS-2017-Unsupervised-trainval-480p.zip