This reporsitory contains the annotations and scripts for the PerVL benchmarks reported in the paper
"This is my unicorn, Fluffy": Personalizing frozen vision-language representations , Niv Cohen, Rinon Gal, Eli A. Meirom, Gal Chechik, Yuval Atzmon, ECCV 2022 (Oral)
- Make sure you have miniconda or anaconda installed
- Install and activate the project conda environment
conda env create --file ./environment.yaml
conda activate palavra
pip install git+https://github.com/openai/CLIP.git
- Get the Youtube-VOS data from its official website: https://youtube-vos.org/dataset/vos/ (2019 version)
- Put the train.zip under the directory data/youtube_vos and unzip
- Prepare YTVOS initial directory structure:
cd ytvos
python prepare_YTVOS_initial_directory_structure.py
python ytvos_read_split.py
- Get the DeepFashion2 train data from its official website: https://github.com/switchablenorms/DeepFashion2
- Put the train.zip filer under the directory data/deep_fashion2 and unzip
- Prepare deep_fashion initial directory structure:
cd deep_fashion
python prepare_deep_fashion2_initial_directory_structure.py
- Download the parsed caption file from this project:
https://github.com/yuvalatzmon/compositional-relationships-data/blob/master/mscoco-train2014-svo-2016-02-18.txt
and save under the directory:data/inversion_model_train_data - Parse the ms-coco annotation file to choose the top common classes:
cd inversion_training
python parse_mscoco_txt.py
-
Download ms-coco 2014 train set from here: https://cocodataset.org/#download and unzip it to under
data/inversion_model_train_data -
Extract visual features using the CLIP network:
cd inversion_training
python get_visual_features.py
- Download open-images class names from:
https://storage.googleapis.com/openimages/v6/oidv6-class-descriptions.csv
and save under the directory:data/inversion_model_train_data - Find matches between chosen ms-coco classes and open-images class names by running:
python text_from_open_images.py
| directory | file | description |
|---|---|---|
annotations/ |
* | CSV and YAML files with the split and annotation we prepared for PerVL-DeepFashion2 and PerVL-YTVOS. |
deep_fashion/ |
* | Scripts to create a folder strucutre for the deep_fashion2 benchmark |
ytvos/ |
* | Scripts to create a folder strucutre for the youtube-vos benchmark |
inversion_training/ |
* | Scripts to preprocess ms-coco data for training an inversion module |
Copyright (C) 2022 NVIDIA Corporation. All rights reserved.
The benchmark and annotations are made available under the CC-BY-NC-SA-4.0. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. To view a copy of this license, visit the LICENSE file in this repository root.
For license information regarding the DeepFashion2 dataset and its annotations, please refer to the official website.
YouTube-VOS dataset annotations were released under CC-BY-4.0. For license information regarding the YouTube-VOS dataset and its annotations, please refer to the official website.
COCO dataset annotations were released under CC-BY-4.0. For license information regarding the COCO dataset and its annotations, please refer to the official website.
compositional-relationships-data were released under CC-BY-4.0. For license information regarding the compositional-relationships-data repository, please refer to the official website.
OpenImages annotations were released under CC-BY-4.0. For license information regarding OpenImages dataset, please refer to the official website.
If you use the contents of this project, please cite our paper.
@inproceedings{eccv2022_palavra_cohen,
author = {Cohen, Niv and Gal, Rinon and Meirom, Eli A. and Chechik, Gal and Atzmon, Yuval},
booktitle = {European Conference on Computer Vision (ECCV) },
title = {"This is my unicorn, Fluffy": Personalizing frozen vision-language representations},
year = {2022}
}
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