LLAVA数据集下载

1. Data

Data file name	Size
llava_instruct_150k.json	229 MB
llava_instruct_80k.json	229 MB
conversation_58k.json	126 MB
detail_23k.json	20.5 MB
complex_reasoning_77k.json	79.6 MB

1.1 Pretraining Dataset

The pretraining dataset used in this release is a subset of CC-3M dataset, filtered with a more balanced concept coverage distribution. Please see here for a detailed description of the dataset structure and how to download the images.

If you already have CC-3M dataset on your disk, the image names follow this format: GCC_train_000000000.jpg. You may edit the image field correspondingly if necessary.

Data	Chat File	Meta Data	Size
CC-3M Concept-balanced 595K	chat.json	metadata.json	211 MB
LAION/CC/SBU BLIP-Caption Concept-balanced 558K	blip_laion_cc_sbu_558k.json	[metadata.json](#Data Chat File Meta Data Size CC-3M Concept-balanced 595K chat.json metadata.json 211 MB LAION/CC/SBU BLIP-Caption Concept-balanced 558K blip_laion_cc_sbu_558k.json metadata.json 181 MB)	181 MB

Important notice : Upon the request from the community, as ~15% images of the original CC-3M dataset are no longer accessible, we upload images.zip for better reproducing our work in research community. It must not be used for any other purposes. The use of these images must comply with the CC-3M license. This may be taken down at any time when requested by the original CC-3M dataset owner or owners of the referenced images.

1.2 GPT-4 Prompts

We provide our prompts and few-shot samples for GPT-4 queries, to better facilitate research in this domain. Please check out the prompts folder for three kinds of questions: conversation, detail description, and complex reasoning.

They are organized in a format of system_message.txt for system message, pairs of abc_caps.txt for few-shot sample user input, and abc_conv.txt for few-shot sample reference output.

Note that you may find them in different format. For example, conversation is in jsonl, and detail description is answer-only. The selected format in our preliminary experiments works slightly better than a limited set of alternatives that we tried: jsonl, more natural format, answer-only. If interested, you may try other variants or conduct more careful study in this. Contributions are welcomed!

2. Visual Instruction Tuning

---------2.1 指令调整数据（instruction tuning data）---------:

LLaVA-Instruct-150K

官方：llava_v1_5_mix665k.json

---------2.2 图像（images）---------

COCO

官方：train2017

GQA

官方：images

OCR-VAQ

官方：download script
多线程下载（速度更快） ：Github解决方案以及 CSDN解决方案
处理好的数据集下载（方便快捷） ：Huggingface

TextVQA

官方：train_val_images

VisualGenome

官方：part1, part2

复制代码

playground
	├──data
	│	├── coco
	│	│   └── train2017
	│	├── gqa
	│	│   └── images
	│	├── ocr_vqa
	│	│   └── images
	│	├── textvqa
	│	│   └── train_images
	│	└── vg
	│	    ├── VG_100K
	│	    └── VG_100K_2
	└── ...

3. Pretrained Model

---------3.1 语言大模型---------
vicuna-13b-v1.5
vicuna-7b-v1.5
---------3.2 视觉大模型---------
clip-vit-large-patch14-336
---------3.3 LLAVA-1.5预训练模型---------
LLAVA-1.5-13b
LLAVA-1.5-7b
---------3.4 LLAVA-lora微调训练的模型---------
LLAVA-1.5--13b-lora
LLAVA-1.5--7b-lora