metadata

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
      dtype: string
  splits:
    - name: dcs_sa
      num_bytes: 1192380951
      num_examples: 2294572
    - name: dcs_mc
      num_bytes: 1313184418
      num_examples: 2294572
    - name: dcm_sa_2_img
      num_bytes: 858402949
      num_examples: 1400000
    - name: dcm_mc_2_img
      num_bytes: 931128693
      num_examples: 1400000
    - name: dcm_sa_3_img
      num_bytes: 1167523949
      num_examples: 1400000
    - name: dcm_mc_3_img
      num_bytes: 1297530106
      num_examples: 1400000
    - name: dcm_sa_4_img
      num_bytes: 1435043372
      num_examples: 1400000
    - name: dcm_mc_4_img
      num_bytes: 1596677323
      num_examples: 1400000
    - name: vgs_sa
      num_bytes: 595577425
      num_examples: 1537630
    - name: vgs_mc
      num_bytes: 671343503
      num_examples: 1537630
    - name: vgm_sa_2_img
      num_bytes: 536078137
      num_examples: 1400000
    - name: vgm_mc_2_img
      num_bytes: 612895409
      num_examples: 1400000
    - name: vgm_sa_3_img
      num_bytes: 693450488
      num_examples: 1400000
    - name: vgm_mc_3_img
      num_bytes: 830159021
      num_examples: 1400000
    - name: vgm_sa_4_img
      num_bytes: 802710456
      num_examples: 1400000
    - name: vgm_mc_4_img
      num_bytes: 972149375
      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-*
      - split: vgm_mc_3_img
        path: data/vgm_mc_3_img-*
      - split: vgm_sa_4_img
        path: data/vgm_sa_4_img-*
      - split: vgm_mc_4_img
        path: data/vgm_mc_4_img-*
task_categories:
  - question-answering
language:
  - en
tags:
  - multimodal
size_categories:
  - 10M<n<100M

ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models

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.

Dataset Details

Dataset Sources

Repository: https://github.com/JieyuZ2/ProVision
Paper:
Blog:
Source Data: Visual Genome/GQA and DataComp

Uses

Direct Use

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

Out-of-Scope Use

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.

Datasets:

Salesforce
/

ProVision-10M