Awesome

PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction (Official Repository) <a name="pqpp"></a>

This repository contains the implementation and dataset for the scientific paper "PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction." We have compiled over 1.5 million prompt and query annotations from more than 270 annotators.

The dataset includes human annotations evaluating image retrieval performance using BLIP2 and CLIP, as well as prompt performance in generative models such as GLIDE and SDXL.

Table of contents <a name="table-of-contents"></a>

• PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction
  • Citation
  • About
  • Note
  • Data Card
    • Dataset Overview
    • Dataset Folder Description
  • Project Structure
  • Getting Started
    • Installing Pre-requisites
  • Usage
    • Retrieval Models
    • Prediction Models
  • Complete Benchmark
    • Retrieval
    • Generative
  • Developed with
  • Acknowledgements
  • License

Citation <a name="citation"></a>

Please cite our work if you use any material released in this repository.

@article{Poesina-Arxiv-2024,
title="{PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction}",
author={Eduard Poesina and Adriana Valentina Costache and Adrian-Gabriel Chifu and Josiane Mothe and Radu Tudor Ionescu},
year={2024},
journal={arXiv preprint arXiv:2406.04746},
}

About <a name="about"></a>

This repository hosts the annotated dataset and the implementations of the prediction models described in the original paper. This file also hosts the extended benchmark, including models which did not pass a minimum correlation threshold of 0.1.

We add instructions and full code in order to allow other researchers to easily replicate our research or validate our approach. You can find instructions for installation and training models. We provide ground truth data for both generative and retrieval formats in order to facilitate integrations within your own code.

Note <a name="note"></a>

If you are interested to conduct your own research on the dataset (or any of the retrieval/generative setting models) you can download the generated images and original MS COCO used images at:

https://fmiunibuc-my.sharepoint.com/:u:/g/personal/radu_ionescu_fmi_unibuc_ro/Eb0peYyLDVRNn0EPeY7ZwKUBAd4Yt-Zs_PtEpc-DmQ0P4A?e=oIflTJ.

Data Card <a name="data-card"></a>

Dataset Overview <a name="dataset-overview"></a>

Data Subject(s)

• Non-Sensitive Data about people (Contains Original Images from MS COCO)

Dataset Snapshot

Content Description

id - number, id of the query in MS COCO
image_id - number, id of original image
best_caption - string, text containing selected prompt
blip2_rr - float, reciprocal rank for query using blip2 retrieval method
clip_rr - float, reciprocal rank for query using clip retrieval method
retrieval_avg_rr - float, average of the reciprocal rank scores of both retrieval models
blip2_pk - float, precision @ 10 for the query using blip2 retrieval method
clip_pk - float, precision @ 10 for the query using clip retrieval method
retrieval_avg_pk - float, average of the precision @ 10 scores of both retrieval methods
glide_score - human annotated generative score for the glide model
sdxl_score - human annotated generative score for the sdxl model
avg_generative_score - average of the human annotated generative scores

Typical Data Point

id,image_id,best_caption,blip2_rr,clip_rr,retrieval_avg_rr,blip2_pk,clip_pk,retrieval_avg_pk,glide_score,sdxl_score,avg_generative_score
319365,363951,Black and white of windsurfers on a lake.,1.0,1.0,1.0,0.1,0.1,0.1,0.5,2.0,1.25

Dataset Folder Description <a name="dataset-description"></a>

Our dataset consists of two major parts:

    1. dataset/train.csv
       dataset/validation.csv
       dataset/test.csv 
      (containing MS COCO image id, p@10 /RR scores for retrieval setting, and the scores for the generative setting)
    2. image folder (contains the SDXL/GLIDE generated images alongside the original MS COCO image). This must be downloaded from the extra [resources](https://fmiunibuc-my.sharepoint.com/:u:/g/personal/radu_ionescu_fmi_unibuc_ro/Eb0peYyLDVRNn0EPeY7ZwKUBAd4Yt-Zs_PtEpc-DmQ0P4A?e=oIflTJ).

The image folder has the following structure:

    images\
        {IMG_ID_1}\
            \image_4.png - Image Generated by SDXL 
            \image_5.png - Image Generated by SDXL 
            \image_6.png - Ground Truth, Original MS-COCO Image
            \image_7.png - Image Generated by GLIDE
            \image_8.png - Image Generated by GLIDE
        {IMG_ID_2}\
        .
        .
        .        
        {IMG_ID_N}\

The suffixes _4, _5 denote generation by SDXL. The suffix _6 denotes the MS-COCO dataset. The suffixes _7, _8 denote generation by GLIDE.

Project Structure <a name="project-structure"></a>

The project is structured as follows:

\PQPP
    \dataset - folder containing the annotated dataset
        \generative_annotation_scores
            \ gt_for_generative_all_models.csv - Score for each query in the generative setting as described in the paper.
            \ gt_for_generative_glide_new.csv - Score for each query of the glide method.
            \ gt_for_generative_sdxl.csv - Score for each query of SDXL method.
        \retrieval_model_scores
            \ avg_scores_rr.pickle - the average rr score per query of the two retrieval models.
            \ avg_scores_p10.pickle - the average p@10 score per query of the two retrieval models.
            \ blip_2_rr_scores_map.pickle - the rr score per query of the BLIP2 model.
            \ blip_2_pk_scores_map.pickle - the P@10 score per query of the BLIP2 model.
            \ clip_rr_scores.pickle - the rr score per query for the CLIP model.
            \ clip_pk_scores.pickle - the p@10 score per query for the CLIP model.
        \ all_users_ann_new.csv - Original generative setting annotations. Anonymized file
        \ best_captions_df.pickle - File containing extra information about each query caption

        === Files to be used for training new models or replicating the current CSV files include query caption, image ids, scores for the retrieval and generative settings === 

        \ dataset.csv - Centralized ground truth file which contains all data.
        \ retrieval_groundtruth.pickle - File containing manual relevant ids for each query.
        \ train.csv - File containing training data split. (60% of data)
        \ validation.csv - Centralized ground truth file for validation data. (20% of data)
        \ test.csv - Centralized ground truth file for test data.  (20% of data)

    \pipelines - folder containing scripts to generate images for the generative setting
    \predictors - folder containing performance predictors as described in the paper
        \ correlation_cnn - Contains the CNN-based approach inspired by Sun. et al
        \ finetuned_bert - Contains the finetuned BERT model training script
        \ linguistic_features - Contains the linguistic features model training script
        \ finetuned_clip - Contains the finetuned CLIP model training script described in our research
        \ neural_embeddings - Contains the implementation of Arabzadeh et al.
        \ query_drift - Contains script to implement query drift.
        \ score-variation - Contains the score-variation script
    \retrieval_model_annotations - folder containing scripts to kickstart annotation process for retrieval and train automatic retrieval groundtruth computation
    \retrieval_models - folder containing scripts to perform text-to-image search

Getting Started <a name="getting-started"></a>

Instructions

Dataset Research

1. Clone the GitHub from the official repo.
2. Download the support files.
3. Unarchive the content inside the base repo folder.

Loading data

import pandas as pd

# Read the CSV file
df = pd.read_csv('./dataset/test.csv')

# Map the image paths for each row
df['sdxl1'] = df['image_id'].apply(lambda x: f'images/{x}/image_4.png')
df['sdxl2'] = df['image_id'].apply(lambda x: f'images/{x}/image_5.png')
df['gt_image'] = df['image_id'].apply(lambda x: f'images/{x}/image_6.png')
df['glide_1'] = df['image_id'].apply(lambda x: f'images/{x}/image_7.png')
df['glide_2'] = df['image_id'].apply(lambda x: f'images/{x}/image_8.png')

# Print the first row
print(df.iloc[0])

Installing Pre-requisites <a name="prereqs"></a>

In order to run our models you will need to install the requirements found in requirements.txt

pip install -r requirements.txt

Usage <a name="usage"></a>

Retrieval Models <a name="retrieval-models"></a>

The retrieval models can be found at:

\retrieval_models
    \clip
        \clip_retrieval.py - CLIP Retrieval Run. Saves Image ids returned.
        \save_clip_scores.py - CLIP Retrieval Run. Saves the scores for the retrieval.
        \generate_clip_embeddings.py - Generates CLIP embeddings for the MS-COCO dataset.
        \generate_clip_query_embeddings.py - Generates CLIP embeddings for queries.

    \blip2
        \blip2_retrieval.py - BLIP-2 Retrieval Run. Saves Image ids returned.
        \save_blip2_scores.py - BLIP-2 Retrieval Run. Saves the scores for the retrieval.
        \generate_blip2_embeddings.py - Generates BLIP-2 embeddings for the MS-COCO image dataset.
        \generate_blip2_query_embeddings.py - Generates BLIP-2 embeddings for queries.

    \compute_scores.py - Allows the computation of P@10 and RR scores.
    \generate_average_scores.py - Computes the average of the computed scores (CLIP/BLIP-2).

Prediction Models <a name="prediction-models"></a>

Prediction models can be found at:

\predictors
    \correlation_cnn
    \finetuned_clip
    \neural_embeddings
    \score-variation

Paper Reproduction

Retrieval-Models Results Generation:

1. Download the extra resource containing the processed retrieval groundtruth.
2. If you wish to reproduce the retrieval models, go to the retrieval models folder and select the preferred model.
3. Run python {model}_retrieval.py. This will allow you to rerun the selected model retrieval process.
4. Run python save_{model}_scores.py. This will compute the scores of the retrieval model and save it to the appropriate location.

Domain(s) of Application

Machine Learning, Computer Vision, Query Performance Prediction, Prompt Performance Prediction, Retrieval Models, Generative Models

Provenance

Collection

Method(s) Used

• Crowdsourced - Volunteer

Dataset Version and Maintenance

Maintenance Status

Actively Maintained - No new versions will be made available, but this dataset will be actively maintained, including but not limited to updates to the data.

Version Details

Current Version: 1.0 Last Updated: 05/2024 Release Date: 05/2024

Complete Benchmark <a name="benchmark"></a>

Retrieval <a name="benchmark-ret"></a>

Generative <a name="benchmark-gen"></a>

Developed with <a name="developed-with"></a>

Annotation platform was developed with the following technologies:

Research was conducted using the following technologies:

OpenAI CLIP Model https://github.com/openai/CLIP

OpenAI GLIDE Model https://github.com/openai/glide-text2im

Salesforce BLIP-2 Model https://github.com/salesforce/LAVIS/tree/main/projects/blip2

StabilityAI SDXL Model https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0

🎉 Acknowledgements <a name="acknowledgement"></a>

We thank all researchers for their implication and expertise and annotators for their incredible work amounting to our dataset.

License <a name="license"></a>

The MS COCO annotations are released under a Creative Commons Attribution 4.0 License https://cocodataset.org/#termsofuse. The MS COCO images are subject to Flickr Terms of Use https://www.flickr.com/creativecommons/

We release our annotations and generated images maintaining the Creative Commons Attribution 4.0 License https://cocodataset.org/#termsofuse CC BY 4.0