metadata

license: cc-by-nc-4.0
dataset_info:
  features:
    - name: id
      dtype: string
    - name: images
      sequence: string
    - name: metadata
      struct:
        - name: dataset
          dtype: string
        - name: task_instruction
          dtype: string
    - name: conversation
      list:
        - name: content
          dtype: string
        - name: role
          dtype: string
  splits:
    - name: cota_293k
      num_bytes: 684640621
      num_examples: 293105
    - name: cota_815k
      num_bytes: 1643764353
      num_examples: 815582
  download_size: 327551290
  dataset_size: 2328404974
configs:
  - config_name: default
    data_files:
      - split: cota_293k
        path: data/cota_293k-*
      - split: cota_815k
        path: data/cota_815k-*

🌮 TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action

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Summary

TLDR: CoTA is a large-scale dataset of synthetic Chains-of-Thought-and-Action (CoTA) generated by multi-modal large language models.

Load data

from datasets import load_dataset
dataset = load_dataset("agentstudio-family/cota-mantis", split="cota_293k")

Dataset Card

Dataset Details

This dataset contains synthetic chains of thoughts and actions involving 15 actions：OCR, LocalizeObjects, GetObjects, EstimateRegionDepth, EstimateObjectDepth, Crop, ZoomIn, QueryLanguageModel, GetImageToImagesSimilarity, GetImageToTextsSimilarity, GetTextToImagesSimilarity, DetectFaces, QueryKnowledgeBase, Calculate, and SolveMathEquation. Additionally, the Terminate action is added for the model to provide a final answer. You can find the detailed statistics of this dataset, including the data sources distribution, the average and max number of images and turns below:

Uses

The intended use of this dataset is to finetune multi-modal language models to produce chains of thoughts and actions to answer difficult and complex visual questions.

Direct Use

You can directly use this dataset to train Mantis-based models with our codebase. To train LLaVA-OneVision models, please use cota-llava in the collection. To train other multi-modal language models, you might need to adapt the conversation format to work for your particular models.

Out-of-Scope Use

This dataset should not be used for testing models.

Source Data

The source data comes from Cauldron and Mantis-Instruct. They are collected from various existing datasets, including COCO, AOKVQA, ScienceQA, Visual Genome, etc.

Data Collection and Processing

Bias, Risks, and Limitations

Our dataset has the following limitations:

The chains of thoughts and actions are generated by gpt-4o-2024-08-06 and thus inherit its biases;
The actions are somewhat limited as they cover mostly vision-centric tools such as DepthEstimation and some generic tools such as QueryKnowledgeBase.
Please refer to the paper for additional limitations.

License

The CoTA datasets are licensed under the noncommerical license CC-BY-NC 4.0. Users need to make their own assessment regarding any obligations or responsibilities under the corresponding licenses or terms and conditions pertaining to the original datasets and data. This release is for research purposes only in support of an academic paper.

Citation

@misc{ma2024tacolearningmultimodalaction,
      title={TACO: Learning Multi-modal Action Models with Synthetic Chains-of-Thought-and-Action}, 
      author={Zixian Ma and Jianguo Zhang and Zhiwei Liu and Jieyu Zhang and Juntao Tan and Manli Shu and Juan Carlos Niebles and Shelby Heinecke and Huan Wang and Caiming Xiong and Ranjay Krishna and Silvio Savarese},
      year={2024},
      eprint={2412.05479},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.05479}, 
}