--- license: cc-by-nc-4.0 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 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.** ![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 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 CC-BY-NC-2.0 license. ## Citation ``` ```