Datasets:
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
fine-grained-recognition
large-vision-language-models
benchmark
image-retrieval
visual-question-answering
License:
| pretty_name: FG-BMK | |
| license: other | |
| language: | |
| - en | |
| task_categories: | |
| - image-classification | |
| - visual-question-answering | |
| tags: | |
| - fine-grained-recognition | |
| - large-vision-language-models | |
| - benchmark | |
| - image-retrieval | |
| - visual-question-answering | |
| size_categories: | |
| - 100K<n<1M | |
| # FG-BMK | |
| FG-BMK is a fine-grained evaluation benchmark for large vision-language models and vision-language models. It contains benchmark metadata, generated questions, train/test splits, class lists, and evaluation code for studying fine-grained visual understanding from both semantic and feature-representation perspectives. | |
| The benchmark covers 1.01 million questions over 0.28 million images. It evaluates two complementary settings: | |
| - Human-oriented evaluation: dialogue-style fine-grained visual questions, including hierarchical category recognition, attribute recognition, and knowledge-bias estimation. | |
| - Machine-oriented evaluation: fine-grained recognition and image retrieval using predefined train/test splits and class files. | |
| This repository contains the benchmark files and evaluation code. Image archives are hosted separately as companion Hugging Face dataset repositories. | |
| ## Companion Image Repositories | |
| | Dataset subset | Repository | Archive | | |
| |---|---|---| | |
| | FGVC-Aircraft | [SEU-VIPGroup/FG-BMK-image-Aircraft](https://huggingface.co/datasets/SEU-VIPGroup/FG-BMK-image-Aircraft) | `aircraft.zip` | | |
| | CUB-200-2011 | [SEU-VIPGroup/FG-BMK-image-CUB](https://huggingface.co/datasets/SEU-VIPGroup/FG-BMK-image-CUB) | `CUB.zip` | | |
| | DeepFashion | [SEU-VIPGroup/FG-BMK-image-DeepFashion](https://huggingface.co/datasets/SEU-VIPGroup/FG-BMK-image-DeepFashion) | `deepfashion.tar` | | |
| | Oxford 102 Flowers | [SEU-VIPGroup/FG-BMK-image-Flowers102](https://huggingface.co/datasets/SEU-VIPGroup/FG-BMK-image-Flowers102) | `flowers102.tar` | | |
| | Food-101 | [SEU-VIPGroup/FG-BMK-image-Food101](https://huggingface.co/datasets/SEU-VIPGroup/FG-BMK-image-Food101) | `food101.tar` | | |
| | iNat2021 validation images | [SEU-VIPGroup/FG-BMK-image-Val](https://huggingface.co/datasets/SEU-VIPGroup/FG-BMK-image-Val) | `val.tar.gz` | | |
| | SkinCon | [SEU-VIPGroup/FG-BMK-image-SkinCon](https://huggingface.co/datasets/SEU-VIPGroup/FG-BMK-image-SkinCon) | `skincon.tar` | | |
| | Stanford Dogs | [SEU-VIPGroup/FG-BMK-image-Dog](https://huggingface.co/datasets/SEU-VIPGroup/FG-BMK-image-Dog) | `dog.zip` | | |
| | VegFru | [SEU-VIPGroup/FG-BMK-image-VegFru](https://huggingface.co/datasets/SEU-VIPGroup/FG-BMK-image-VegFru) | `vegfru.zip` | | |
| Other subsets referenced by the benchmark should be obtained from their original dataset sources when they are not mirrored in the companion repositories. | |
| ## Repository Contents | |
| - `benchmark/human-oriented/`: question files for human-oriented LVLM evaluation. | |
| - `benchmark/machine-oriented/`: class lists and train/test splits for recognition and retrieval evaluation. | |
| - `demo/human_evaluation/`: example inference and answer-scoring code. | |
| - `demo/machine_evaluation/`: example feature extraction, linear evaluation, and retrieval evaluation code. | |
| - `static/` and project assets: figures and supporting files for the benchmark release. | |
| ## Basic Use | |
| 1. Download this repository. | |
| 2. Download the needed image archive from the companion repository above. | |
| 3. Extract the archive locally. | |
| 4. Point the evaluation scripts to the extracted image folder and to the corresponding FG-BMK question or split file. | |
| For human-oriented evaluation, use files under `benchmark/human-oriented/` and run the demo code in `demo/human_evaluation/`. | |
| For machine-oriented evaluation, use files under `benchmark/machine-oriented/<dataset>/` and run the demo code in `demo/machine_evaluation/`. | |
| ## Paper and Code | |
| - Project page: https://fg-bmk.github.io/ | |
| - Code repository: https://github.com/SEU-VIPGroup/FG-BMK | |
| - arXiv extended version: https://arxiv.org/abs/2606.19053 | |
| - arXiv ICLR version: https://arxiv.org/abs/2504.14988 | |
| ## Citation | |
| ```bibtex | |
| @article{yu2026fgbmk, | |
| title = {Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: From Evaluation to Diagnosis}, | |
| author = {Yu, Hong-Tao and Xie, Chen-Wei and Peng, Yuxin and Belongie, Serge and Wei, Xiu-Shen}, | |
| journal = {arXiv preprint arXiv:2606.19053}, | |
| year = {2026} | |
| } | |
| @inproceedings{yu2026fgbmk_iclr, | |
| title = {Benchmarking Large Vision-Language Models on Fine-Grained Image Tasks: A Comprehensive Evaluation}, | |
| author = {Yu, Hong-Tao and Peng, Yuxin and Belongie, Serge and Wei, Xiu-Shen}, | |
| booktitle = {International Conference on Learning Representations (ICLR)}, | |
| year = {2026} | |
| } | |
| ``` | |
| ## License and Data Terms | |
| The benchmark metadata and evaluation code follow the terms provided by the project authors. Image archives are derived from their corresponding source datasets; users must also follow the license and usage terms of each original dataset. | |