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--- |
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dataset_info: |
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features: |
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- name: id |
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dtype: string |
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- name: question |
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dtype: string |
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- name: options |
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list: string |
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- name: answer |
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dtype: string |
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- name: task_plan |
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dtype: string |
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- name: image |
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dtype: image |
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splits: |
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- name: 3d_how_many |
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num_bytes: 964232493.0 |
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num_examples: 654 |
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- name: 3d_what |
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num_bytes: 944850246.0 |
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num_examples: 645 |
|
- name: 3d_where |
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num_bytes: 989034725.0 |
|
num_examples: 669 |
|
- name: 3d_what_attribute |
|
num_bytes: 931184419.0 |
|
num_examples: 639 |
|
- name: 3d_where_attribute |
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num_bytes: 897312251.0 |
|
num_examples: 609 |
|
- name: 3d_what_distance |
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num_bytes: 836764094.0 |
|
num_examples: 585 |
|
- name: 3d_where_distance |
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num_bytes: 925465404.0 |
|
num_examples: 645 |
|
- name: 3d_what_attribute_distance |
|
num_bytes: 970396774.0 |
|
num_examples: 678 |
|
- name: 3d_what_size |
|
num_bytes: 988177167.0 |
|
num_examples: 675 |
|
- name: 3d_where_size |
|
num_bytes: 898574558.0 |
|
num_examples: 618 |
|
- name: 3d_what_attribute_size |
|
num_bytes: 993251978.0 |
|
num_examples: 678 |
|
- name: 2d_how_many |
|
num_bytes: 40708392.0 |
|
num_examples: 606 |
|
- name: 2d_what |
|
num_bytes: 46567124.0 |
|
num_examples: 681 |
|
- name: 2d_where |
|
num_bytes: 47803083.0 |
|
num_examples: 699 |
|
- name: 2d_what_attribute |
|
num_bytes: 46026755.0 |
|
num_examples: 657 |
|
- name: 2d_where_attribute |
|
num_bytes: 47675852.0 |
|
num_examples: 636 |
|
- name: sg_what_object |
|
num_bytes: 24281703.0 |
|
num_examples: 633 |
|
- name: sg_what_attribute |
|
num_bytes: 26390284.0 |
|
num_examples: 645 |
|
- name: sg_what_relation |
|
num_bytes: 27153148.0 |
|
num_examples: 618 |
|
download_size: 10589322704 |
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dataset_size: 10645850450.0 |
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configs: |
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- config_name: default |
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data_files: |
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- split: 3d_how_many |
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path: data/3d_how_many-* |
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- split: 3d_what |
|
path: data/3d_what-* |
|
- split: 3d_where |
|
path: data/3d_where-* |
|
- split: 3d_what_attribute |
|
path: data/3d_what_attribute-* |
|
- split: 3d_where_attribute |
|
path: data/3d_where_attribute-* |
|
- split: 3d_what_distance |
|
path: data/3d_what_distance-* |
|
- split: 3d_where_distance |
|
path: data/3d_where_distance-* |
|
- split: 3d_what_attribute_distance |
|
path: data/3d_what_attribute_distance-* |
|
- split: 3d_what_size |
|
path: data/3d_what_size-* |
|
- split: 3d_where_size |
|
path: data/3d_where_size-* |
|
- split: 3d_what_attribute_size |
|
path: data/3d_what_attribute_size-* |
|
- split: 2d_how_many |
|
path: data/2d_how_many-* |
|
- split: 2d_what |
|
path: data/2d_what-* |
|
- split: 2d_where |
|
path: data/2d_where-* |
|
- split: 2d_what_attribute |
|
path: data/2d_what_attribute-* |
|
- split: 2d_where_attribute |
|
path: data/2d_where_attribute-* |
|
- split: sg_what_object |
|
path: data/sg_what_object-* |
|
- split: sg_what_attribute |
|
path: data/sg_what_attribute-* |
|
- split: sg_what_relation |
|
path: data/sg_what_relation-* |
|
--- |
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# Dataset Card for TaskMeAnything-v1-imageqa-2024 |
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<h2 align="center"> TaskMeAnything-v1-imageqa-2024 benchmark dataset</h2> |
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<h2 align="center"> <a href="https://www.task-me-anything.org/">🌐 Website</a> | <a href="https://arxiv.org/abs/2406.11775">📑 Paper</a> | <a href="https://huggingface.co/collections/jieyuz2/taskmeanything-664ebf028ab2524c0380526a">🤗 Huggingface</a> | <a href="https://huggingface.co/spaces/zixianma/TaskMeAnything-UI">💻 Interface</a></h2> |
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<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update. </h2> |
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## TaskMeAnything-v1-2024 |
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[TaskMeAnything-v1-imageqa-2024](https://huggingface.co/datasets/weikaih/TaskMeAnything-v1-imageqa-2024) is a benchmark for reflecting the current progress of MLMs by `automatically` finding tasks that SOTA MLMs struggle with using the TaskMeAnything Top-K queries. |
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This benchmark includes 3,279 2d questions, 7,095 3d questions, and 1,896 real image questions that the TaskMeAnything algorithm automatically approximated as challenging for over 12 popular MLMs. |
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The dataset contains 19 splits, while each splits contains 600+ questions from a specific task generator in TaskMeAnything-v1. For each row of dataset, it includes: image, question, options, answer and its corresponding task plan. |
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## Load TaskMeAnything-v1-2024 ImageQA Dataset |
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``` |
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import datasets |
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dataset_name = 'weikaih/TaskMeAnything-v1-imageqa-2024' |
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dataset = datasets.load_dataset(dataset_name, split = TASK_GENERATOR_SPLIT) |
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``` |
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where `TASK_GENERATOR_SPLIT` is one of the task generators, eg, `2024_2d_how_many`. |
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## Evaluation Results |
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### Overall |
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### Breakdown performance on each task types |
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## Out-of-Scope Use |
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This dataset should not be used for training models. |
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## Disclaimers |
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**TaskMeAnything** and its associated resources are provided for research and educational purposes only. |
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The authors and contributors make no warranties regarding the accuracy or reliability of the data and software. |
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Users are responsible for ensuring their use complies with applicable laws and regulations. |
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The project is not liable for any damages or losses resulting from the use of these resources. |
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## Contact |
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- Jieyu Zhang: jieyuz2@cs.washington.edu |
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## Citation |
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**BibTeX:** |
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```bibtex |
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@article{zhang2024task, |
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title={Task Me Anything}, |
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author={Zhang, Jieyu and Huang, Weikai and Ma, Zixian and Michel, Oscar and He, Dong and Gupta, Tanmay and Ma, Wei-Chiu and Farhadi, Ali and Kembhavi, Aniruddha and Krishna, Ranjay}, |
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journal={arXiv preprint arXiv:2406.11775}, |
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year={2024} |
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} |
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``` |
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