| --- |
| dataset_info: |
| features: |
| - name: question_id |
| dtype: string |
| - name: question |
| dtype: string |
| - name: image |
| dtype: image |
| - name: image_id |
| dtype: string |
| - name: image_classes |
| sequence: string |
| - name: flickr_original_url |
| dtype: string |
| - name: flickr_300k_url |
| dtype: string |
| - name: image_width |
| dtype: int64 |
| - name: image_height |
| dtype: int64 |
| - name: set_name |
| dtype: string |
| - name: image_name |
| dtype: string |
| - name: image_path |
| dtype: string |
| - name: caption_id |
| sequence: int64 |
| - name: caption_str |
| sequence: string |
| - name: reference_strs |
| sequence: string |
| splits: |
| - name: train |
| num_bytes: 6201208209.0 |
| num_examples: 21953 |
| - name: val |
| num_bytes: 919878416.0 |
| num_examples: 3166 |
| - name: test |
| num_bytes: 959971875.0 |
| num_examples: 3289 |
| download_size: 8064165124 |
| dataset_size: 8081058500.0 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: val |
| path: data/val-* |
| - split: test |
| path: data/test-* |
| --- |
| |
|
|
|
|
| <p align="center" width="100%"> |
| <img src="https://i.postimg.cc/g0QRgMVv/WX20240228-113337-2x.png" width="100%" height="80%"> |
| </p> |
|
|
| # Large-scale Multi-modality Models Evaluation Suite |
|
|
| > Accelerating the development of large-scale multi-modality models (LMMs) with `lmms-eval` |
|
|
| 🏠 [Homepage](https://lmms-lab.github.io/) | 📚 [Documentation](docs/README.md) | 🤗 [Huggingface Datasets](https://huggingface.co/lmms-lab) |
|
|
| # This Dataset |
|
|
| This is a formatted version of [TextCaps](https://textvqa.org/textcaps/). It is used in our `lmms-eval` pipeline to allow for one-click evaluations of large multi-modality models. |
|
|
| ``` |
| @inproceedings{sidorov2019textcaps, |
| title={TextCaps: a Dataset for Image Captioningwith Reading Comprehension}, |
| author={Sidorov, Oleksii and Hu, Ronghang and Rohrbach, Marcus and Singh, Amanpreet}, |
| journal={European Conference on Computer Vision}, |
| year={2020} |
| } |
| ``` |
|
|
