---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: question
    dtype: string
  - name: options
    list: string
  - name: answer
    dtype: string
  - name: task_plan
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: 3d_how_many
    num_bytes: 964232493.0
    num_examples: 654
  - name: 3d_what
    num_bytes: 944850246.0
    num_examples: 645
  - name: 3d_where
    num_bytes: 989034725.0
    num_examples: 669
  - name: 3d_what_attribute
    num_bytes: 931184419.0
    num_examples: 639
  - name: 3d_where_attribute
    num_bytes: 897312251.0
    num_examples: 609
  - name: 3d_what_distance
    num_bytes: 836764094.0
    num_examples: 585
  - name: 3d_where_distance
    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
  dataset_size: 10645850450.0
configs:
- config_name: default
  data_files:
  - split: 3d_how_many
    path: data/3d_how_many-*
  - 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-*
---

# Dataset Card for TaskMeAnything-v1-imageqa-2024
<h2 align="center"> TaskMeAnything-v1-imageqa-2024 benchmark dataset</h2>

<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>
    
<h5 align="center"> If you like our project, please give us a star ⭐ on GitHub for latest update.  </h2>

## TaskMeAnything-v1-2024
[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. 
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.


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.

## Load TaskMeAnything-v1-2024 ImageQA Dataset
```
import datasets

dataset_name = 'weikaih/TaskMeAnything-v1-imageqa-2024'
dataset = datasets.load_dataset(dataset_name, split = TASK_GENERATOR_SPLIT)
```
where `TASK_GENERATOR_SPLIT` is one of the task generators, eg, `2024_2d_how_many`.

## Evaluation Results

### Overall

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/_KadJKJSHhZXXfIfePaUg.png)

### Breakdown performance on each task types

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/-DrQ90FuGatJE4CuHsWS9.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/6D33K2tSc1OYF4_f6YJ63.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/eKzh5ghGNVrCluVmnkZW0.png)

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65cb0dcc4913057ac82a7a31/sm8dAmjxsXmJu8oeqLaeQ.png)

## Out-of-Scope Use
This dataset should not be used for training models.


## Disclaimers
**TaskMeAnything** and its associated resources are provided for research and educational purposes only. 
The authors and contributors make no warranties regarding the accuracy or reliability of the data and software. 
Users are responsible for ensuring their use complies with applicable laws and regulations. 
The project is not liable for any damages or losses resulting from the use of these resources.

## Contact

- Jieyu Zhang: jieyuz2@cs.washington.edu

## Citation
**BibTeX:**
```bibtex
@article{zhang2024task,
  title={Task Me Anything},
  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},
  journal={arXiv preprint arXiv:2406.11775},
  year={2024}
}
```