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---
license: apache-2.0
dataset_info:
  features:
  - name: data_path
    sequence: string
  - name: generator
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: options
    sequence: string
  - name: metadata
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  - name: vgm_mc_3_img
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  - name: vgm_sa_4_img
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  - name: vgm_mc_4_img
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    num_examples: 1400000
  download_size: 5904415104
  dataset_size: 15506235575
configs:
- config_name: default
  data_files:
  - split: dcs_sa
    path: data/dcs_sa-*
  - split: dcs_mc
    path: data/dcs_mc-*
  - split: dcm_sa_2_img
    path: data/dcm_sa_2_img-*
  - split: dcm_mc_2_img
    path: data/dcm_mc_2_img-*
  - split: dcm_sa_3_img
    path: data/dcm_sa_3_img-*
  - split: dcm_mc_3_img
    path: data/dcm_mc_3_img-*
  - split: dcm_sa_4_img
    path: data/dcm_sa_4_img-*
  - split: dcm_mc_4_img
    path: data/dcm_mc_4_img-*
  - split: vgs_sa
    path: data/vgs_sa-*
  - split: vgs_mc
    path: data/vgs_mc-*
  - split: vgm_sa_2_img
    path: data/vgm_sa_2_img-*
  - split: vgm_mc_2_img
    path: data/vgm_mc_2_img-*
  - split: vgm_sa_3_img
    path: data/vgm_sa_3_img-*
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    path: data/vgm_mc_3_img-*
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    path: data/vgm_mc_4_img-*
task_categories:
- question-answering
language:
- en
tags:
- multimodal
size_categories:
- 10M<n<100M
---


<h1 align="center">  
  ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
</h1>

ProVision is an extendable data generation engine which produces instruction data for large multimodal language models (MLMs).

In particular, it synthesizes instruction data via data generators (Python programs) and scene graphs rather than proprietary models. It also includes a scene graph generation pipeline consisting of various state-of-the-art models (eg, object detection model). Thus, one can generate instruction data for any given image by first generating the scene graph and then apply data generators.

Provision supports generation of both single-image and multi-image instruction data. One can also extend the engine by adding new data generators.

**You are currently viewing the ProVision-10M dataset.**

![pipeline](pipeline.png)

## Dataset Details

### Dataset Sources

- **Repository**: https://github.com/JieyuZ2/ProVision
- **Paper:** 
- **Blog:**
- **Source Data:**  [Visual Genome](https://homes.cs.washington.edu/~ranjay/visualgenome/index.html)/[GQA](https://cs.stanford.edu/people/dorarad/gqa/about.html) and [DataComp](https://www.datacomp.ai/dcclip/index.html#home)

## Uses

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

ProVision-10M is designed to facilitate research in training multimodal language models.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

ProVision-10M was built to make research into large multimodal models more accessible. Using
the dataset to train models that ingest or generate personally identifying information (such
as images of people’s faces and other sensitive content) as well as military applications are all inappropriate use cases of ProVision-10M.

## Dataset Creation

### Curation Rationale

ProVision-10M was created to demonstrate the potential of programmatically synthesizing instruction data for training multimodal language models.

### Source Data

The dataset is built upon two data sources:

- we use 74,289 images and scene graphs from Visual Genome(the GQA version)
- we use 126,106 images from DataComp

### Dataset summary

**We do not release the images, please download the images from their original sources (GQA/DataComp)**

| Split        | Size     | Format          | Description  |
| :------------| :------  | :------         | :---- |
| vgs_sa       |  1537630 | short answer    | single-image instruction data based on Visual Genome |
| vgs_mc       |  1537630 | multiple choice | single-image instruction data based on Visual Genome |
| vgm_sa_2_img |  1400000 | short answer    | 2-image instruction data based on Visual Genome |
| vgm_mc_2_img |  1400000 | multiple choice | 2-image instruction data based on Visual Genome |
| vgm_sa_3_img |  1400000 | short answer    | 3-image instruction data based on Visual Genome |
| vgm_mc_3_img |  1400000 | multiple choice | 3-image instruction data based on Visual Genome |
| vgm_sa_4_img |  1400000 | short answer    | 4-image instruction data based on Visual Genome |
| vgm_mc_4_img |  1400000 | multiple choice | 4-image instruction data based on Visual Genome |
| dcs_sa       |  2294572 | short answer    | single-image instruction data based on DataComp images |
| dcs_mc       |  2294572 | multiple choice | single-image instruction data based on DataComp images |
| dcm_sa_2_img |  1400000 | short answer    | 2-image instruction data based on DataComp images |
| dcm_mc_2_img |  1400000 | multiple choice | 2-image instruction data based on DataComp images |
| dcm_sa_3_img |  1400000 | short answer    | 3-image instruction data based on DataComp images |
| dcm_mc_3_img |  1400000 | multiple choice | 3-image instruction data based on DataComp images |
| dcm_sa_4_img |  1400000 | short answer    | 4-image instruction data based on DataComp images |
| dcm_mc_4_img |  1400000 | multiple choice | 4-image instruction data based on DataComp images |

## License
We release ProVision-10M under a Apache License 2.0.

## Citation

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