Create README.md

README.md

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