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VL-CheckList

Updates

  • 07/04/2022: VL-CheckList paper on arxiv https://arxiv.org/abs/2207.00221
  • 07/12/2022: Updated object, relation, attribute splits/dataset
  • 08/01/2022: Release the initial code and example models

Introduction

This repository is the official project page for (VL-CheckList). VL-CheckList is an explainable framework that comprehensively evaluates VLP models and facilitates deeper understanding.The current method to evaluate a VLP model is solely by comparing its fine-tuned downstream tasks performance, which has a number of limitations, such as poor interpretability, incomparable results and bias in data.

The core principle of VL-CheckList are: (1) evaluate a VLP model's fundamental capabilities instead of performance on applications (2) disentangle capabilities into relatively independent variables that are easier to analyze.

VL-CheckList evaluates VLP models from three aspects: Object, Attribute and Relationship. We provide the performance quantitative table and the radar chart based on the three aspects.

How to Install VL-CheckList

You can install vl_checklist in your project and import vl_checklist and evaluate your models:

pip install vl_checklist

vilt_test.py is an example code to show how to import vl_checklist in your project.

You need to copy data/ and corpus/ folders to the root of your project and prepare image datasets Link.

You can also clone this project add your model as follows.

git clone https://github.com/om-ai-lab/VL-CheckList.git

Detailed Guidelines How to Evaluate your Model

We include several representative example VLP models in the example_models/ folder.

1. Define a config file e.g. in configs/sample.yaml

MAX_NUM: 2000
MODEL_NAME: "ViLT"
BATCH_SIZE: 4
TASK: "itc"
DATA:
  TYPES: ["Attribute/color"]
  TEST_DATA: ["vg","vaw"]   
OUTPUT: 
  DIR: "output/vilt"

2. Prepare Evaluation Data We provide the initial curated jsons at data/ and corresponding yamls at vl_checklist/corpus. You can need to download image dataset. You can find the instruction in detail Link

3. Load the model which contain predict() and Evaluate class as follows. Please find an example model class Link

4. Run start() as follows

Here is an example code

from example_models.vilt.engine import ViLT
from vl_checklist.evaluate import Evaluate

if __name__ == '__main__':
    model = ViLT('vilt_200k_mlm_itm.ckpt')
    vilt_eval = Evaluate(config="configs/sample.yaml", model=model)
    vilt_eval.start()

5. check the results in the OUTDIR DIR you defined the yaml file You can check the output format LINK

Download Pretrained Weights

We include examples models at example_models/. You can download the pretrained weights at resources/ folder to test our example models:

Demo

We present the demo in huggingface space, you can try it here: Demo link
In this demo, you can change the object and attribute of object in the text prompt. You can also change the size and location of the object.

References

If you use any source codes or datasets included in this toolkit in your work, please cite the following paper. The bibtex are listed below:

@misc{https://doi.org/10.48550/arxiv.2207.00221,
  doi = {10.48550/ARXIV.2207.00221}, 
  url = {https://arxiv.org/abs/2207.00221},
  author = {Zhao, Tiancheng and Zhang, Tianqi and Zhu, Mingwei and Shen, Haozhan and Lee, Kyusong and Lu, Xiaopeng and Yin, Jianwei},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution 4.0 International}
}

@inproceedings{zhao2022explainable,
  title={An explainable toolbox for evaluating pre-trained vision-language models},
  author={Zhao, Tiancheng and Zhang, Tianqi and Zhu, Mingwei and Shen, Haozhan and Lee, Kyusong and Lu, Xiaopeng and Yin, Jianwei},
  booktitle={Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  pages={30--37},
  year={2022}
}