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--- |
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pretty_name: SPEC |
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task_categories: |
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- image-to-text |
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- text-to-image |
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- image-classification |
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tags: |
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- image |
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- text |
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language: |
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- en |
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license: apache-2.0 |
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size_categories: |
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- 1K<n<10K |
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--- |
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# [CVPR 2024] SPEC Benchmark: Evaluating VLMs in Fine-grained and Compositional Understanding |
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introduced in the CVPR 2024 paper [Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding](https://huggingface.co/papers/2312.00081) |
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[**Code**](https://github.com/wjpoom/SPEC) | [**🤗 Paper**](https://huggingface.co/papers/2312.00081) | [**📖 arXiv**](https://arxiv.org/abs/2312.00081) |
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To evaluate the understanding capability of visual-language models on fine-grained concepts, we propose a new benchmark, SPEC, |
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which consists of six distinct subsets, distributed across the dimensions of **S**ize, **P**osition, **E**xistence, and **C**ount. |
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Each test case consists of an image candidate set, which differs only in certain visual concepts, and a text candidate set, |
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which differs only in the corresponding language concept. |
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<p align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/649bce4f200e2dff194d9883/sE65-zVjY_HXUT4-eaqZ9.png" width="90%"/> |
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<be> |
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</p> |
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## 🔧 Usage |
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### install |
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``` shell |
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git clone https://github.com/wjpoom/SPEC.git |
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cd SPEC/ |
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pip install -e . |
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``` |
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### prepare data |
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* run the following code in Python shell, replace `/path/to/save/data` with a specified dir to store the data. |
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```python |
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import zipfile |
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import os |
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from huggingface_hub import hf_hub_download |
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data_root = '/path/to/save/data' |
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hf_hub_download(repo_id='wjpoom/SPEC', repo_type='dataset', filename='data.zip', local_dir=data_root) |
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with zipfile.ZipFile(os.path.join(data_root, 'data.zip'), 'r') as zip_ref: |
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zip_ref.extractall(os.path.join(data_root)) |
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os.remove(os.path.join(data_root, 'data.zip')) |
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``` |
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### explore the dataset |
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* We provide a 📓notebook that enables you to visually explore the test samples in the SPEC dataset. |
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* Run this notebook either [locally](https://github.com/wjpoom/SPEC/blob/main/notebooks/explore_spec_local.ipynb) or online using [Colab](https://colab.research.google.com/github/wjpoom/SPEC/blob/main/notebooks/explore_spec_colab.ipynb). |
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### reproduce the results |
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* In our paper, we evaluated four popular VLMs using our SPEC dataset, namely: CLIP, BLIP, FLAVA and CoCa. |
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* To reproduce the results with these VLMs, you can run [this script](https://github.com/wjpoom/SPEC/blob/main/spec/run_eval.sh). |
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* You can also reproduce with this [local notebook](https://github.com/wjpoom/SPEC/blob/main/notebooks/evaluate_example_local.ipynb) or the online [Colab notebook](https://colab.research.google.com/github/wjpoom/SPEC/blob/main/notebooks/evaluate_example_colab.ipynb). |
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### evaluate custom VLMs |
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* If you want to evaluate your custom model on SPEC, you can follow the instructions in [this document](https://github.com/wjpoom/SPEC/blob/main/docs/evaluate_custom_model.md). |
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* ## ✒️ Citation |
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If you use our code or data in this repo or find our work helpful, please consider giving a citation: |
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``` |
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@inproceedings{spec2024, |
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title={Synthesize Diagnose and Optimize: Towards Fine-Grained Vision-Language Understanding}, |
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author={Peng, Wujian and Xie, Sicheng and You, Zuyao and Lan, Shiyi and Wu, Zuxuan}, |
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booktitle={CVPR}, |
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year={2024} |
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} |
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``` |
