DataOptim

DataOptim is a data repository designed to offer an optimized solution for utilizing training data in Multimodal Large Language Models (MLLMs) efficiently.

• HuggingFace 🤗: https://huggingface.co/datasets/BAAI/DataOptim

News

• [2024.03.14] Data of TextOCR-GPT4V is now available!
• [2023.12.15] Data of ShareGPT4V is now available!
• [2023.11.06] Data of LLaVA-v1.5 is now available!
• [2023.10.26] VGQA, DocVQA and DVQA are now available!
• [2023.10.17] ScienceQA is now available!

Introduction

Currently, the visual instruction tuning data contain 20 public datasets. More datasets are coming in the future! 🔥🔥🔥

Category	Dataset	Images	Samples	Split
Image captioning	COCO	82783	414113	train
Image captioning	Flickr30K	29000	145000	Karpathy train split
Image captioning	TextCaps	21953	109765	train
Image captioning	TextOCR-GPT4V	25114	25114	train
Visual question answering	VQAv2	82783	443757	train
Visual question answering	OKVQA	8998	9009	train
Visual question answering	OCRVQA	166041	801673	train
Visual question answering	GQA	72140	943000	train
Visual question answering	TextVQA	21953	34602	train
Visual question answering	A-OKVQA	16540	17056	train
Visual question answering	ScienceQA	6218	6218	train
Visual question answering	Visual Genome QA (VGQA)	99280	1445322	-
Visual question answering	DocVQA	10194	39463	train
Visual question answering	DVQA	200000	2325316	train
Grounding	RefCOCO/RefCOCO+/RefCOCOg	24407	287604	train
Grounding	Shikra-RD	883	5922	train
GPT-4 generated	LLaVA-Instruct-150K	81479	157712	-
GPT-4 generated	SVIT	108076	2992799	-
GPT-4V generated	ShareGPT-4V	87296	102025	-
Mixed	LLaVA-v1.51	291684	665298	-
Total		974K2	11.2M

¹ The bounding boxes in LLaVA-v1.5 are based on the padded image. You can find the discussion here.

² The number of images are counted based on image IDs. There might be duplicate images across different image sources.

We use different strategies to collect the prompts for different tasks.

• Image captioning. We carefully collect 5 manually written instructions and randomly sample one as the prompt for each caption. The fourth and fifth instructions are from InstructBLIP.
• Open-ended VQA. As the answers in VQA datasets are generally short, we add an instruction after the question to ask the model to provide answers with a short sentence or phrase.
• Multiple-choice VQA. For A-OKVQA, we add an instruction before the question to ask the model to provide answers with correct options. For ScienceQA, we use the instructions and templates designed by M3IT and randomly sample one to format the prompt. Only data with image context are involved.
• Grounding. For RefCOCO/RefCOCO+/RefCOCOg, we use the data and templates in Shikra and randomly sample one to format the prompt.
• GPT-4/GPT-4V generated & mixed datasets. We keep the prompts unchanged.

Category	Data	Prompts
Image captioning	COCO, Flickr30K, TextCaps, TextOCR-GPT4V	Describe the image as simply as possible with a sentence or phrase. Give a brief summary of what you see. Provide a short description of the image. Write a short description for the image. Briefly describe the content of the image.
Open-ended VQA	VQAv2, OKVQA, OCRVQA, GQA, TextVQA, VGQA, DocVQA, DVQA	question Answer the question directly with a short sentence or phrase.
Multiple-choice VQA	A-OKVQA	Choose the correct option for the following question: question

Quickstart

For the images, you can download the images from our HuggingFace repository or the original websites. If you already have the images, you can skip this process as the image IDs and file names are not changed.

Then unzip and organize the images in following structure.

|- images
  |- coco
    |- COCO_train2014_000000000009.jpg
    |- ...
  |- coco_2017
    |- 000000274591.jpg
    |- ...
  |- docvqa
    |- ffbf0023_4.png
    |- ...
  |- dvqa
    |- ...
  |- filckr30k
    |- 36979.jpg
    |- ...
  |- llava
    |- llava_pretrain
      |- images
  |- ocrvqa
    |- 13714.jpg
    |- ...
  |- open_images
    |- 0a0bc91825468c45.jpg
    |- ...
  |- sam
    |- images
  |- scienceqa
    |- 1
      |- image.png
    |- 2
      |- image.png
    |- ...
  |- share_textvqa
    |- images
  |- visual_genome
    |- 1.jpg
    |- ...
  |- web-celebrity
    |- images
  |- web-landmark
    |- images
  |- wikiart
    |- images

After that, you can use this diretory as the --image_folder in LLaVA's training script.

For the visual instruction tuning QAs, all of the data mentioned above are already converted to the training format of LLaVA in our HuggingFace repository. You can download them directly from HuggingFace.

For referring QAs, the bounding box is in the form of [x1, y1, x2, y2], corresponding to the top left x, top left y, bottom right x and bottom right y. The values are float numbers normalized to [0, 1], based on the size of original images, except LLaVA-v1.5, which is based on the padded image (see more discussion here). We provide a script here to expand the bounding boxes to square.

Contact

If you have any questions, you can open an issue in the GitHub repository or contact [email protected] for more information.