ShareGPT4V: Improving Large Multi-modal Models with Better Captions

⭐️ Our series works: [MMStar] [ShareGPT4Video] [ShareGPT4Omni]

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🚀🚀🚀 Official implementation of ShareGPT4V: Improving Large Multi-modal Models with Better Captions.

• Authors: Lin Chen*, Jinsong Li*, Xiaoyi Dong, Pan Zhang, Conghui He, Jiaqi Wang, Feng Zhao📧, Dahua Lin📧

• Institutes: University of Science and Technology of China; Shanghai AI Laboratory

• Resources: [Paper] [Project Page] [ShareGPT4V Dataset]

• Models: [ShareGPT4V-7B] [ShareCaptioner]

• ShareGPT4V-7B Demo [OpenXLab] [🤗HuggingFace] [Colab]

• Share-Captioner Demo [OpenXlab] [🤗HuggingFace]

💡 Highlights

• 🔥 A large-scale highly descriptive image-text dataset
• 🔥 100K GPT4-Vision-generated captions, 1.2M high-quality captions
• 🔥 A general image captioner, approaching GPT4-Vision's caption capability.
• 🔥 A superior large multi-modal model, ShareGPT4V-7B

📜 News

[2024/5/8] We released ShareGPT4Video, a large-scale video-caption dataset, with 40K captions annotated by GPT4V and 4.8M captions annotated by our ShareCaptioner-Video. The total videos last with 300 hours and 3000 hours separately!

[2024/4/1] We released an elite vision-indispensable multi-modal benchmark, MMStar. Have fun!🚀

[2023/12/14] We released the ShareGPT4V-13B model. Have fun!🚀

[2023/12/13] Training and evaluation code is available.

[2023/12/13] Local ShareCaptioner is available now! You can utilize it to generate high-quality captions for your dataset with batch inference by directly run tools/share-cap_batch_infer.py.

[2023/11/23] We release the web demo of general Share-Captioner!💥

[2023/11/23] We release code to build your local demo of ShareGPT4V-7B!💥

[2023/11/22] Web demo and checkpoint are available now!💥

[2023/11/21] ShareGPT4V Dataset is available now!💥

[2023/11/20] The paper and project page are released!

👨‍💻 Todo

• Training and evaluation code for ShareGPT4V-7B
• Local ShareCaptioner
• Web demo and local demo of ShareGPT4V-7B
• Checkpoints of ShareGPT4V-7B

🤖 Model Zoo

See more details in ModelZoo.md.

Name	LLM	Checkpoint	LLaVA-Bench-Wild	MME-perception	MME-cognition	MMBench	MMBench-CN	SEED-image	MM-Vet	QBench	SQA-image	VQA-v2	VizWiz	GQA	TextVQA
ShareGPT4V-7B	Vicuna-7B	ShareGPT4V-7B	72.6	1567.4	376.4	68.8	62.2	69.7	37.6	63.4	68.4	80.6	57.2	63.3	60.4
ShareGPT4V-13B	Vicuna-13B	ShareGPT4V-13B	79.9	1618.7	303.2	68.5	63.7	70.8	43.1	65.2	71.2	81.0	55.6	64.8	62.2

Quick Usage

Example Code

from share4v.model.builder import load_pretrained_model
from share4v.mm_utils import get_model_name_from_path
from share4v.eval.run_share4v import eval_model

model_path = "Lin-Chen/ShareGPT4V-7B"

tokenizer, model, image_processor, context_len = load_pretrained_model(
    model_path=model_path,
    model_base=None,
    model_name=get_model_name_from_path(model_path)
)

Check out the details wth the load_pretrained_model function in share4v/model/builder.py.

You can also use the eval_model function in share4v/eval/run_llava.py to get the output easily. By doing so, you can use this code on Colab directly after downloading this repository.

model_path = "Lin-Chen/ShareGPT4V-7B"
prompt = "What is the most common catchphrase of the character on the right?"
image_file = "examples/breaking_bad.png"

args = type('Args', (), {
    "model_path": model_path,
    "model_base": None,
    "model_name": get_model_name_from_path(model_path),
    "query": prompt,
    "conv_mode": None,
    "image_file": image_file,
    "sep": ",",
    "temperature": 0,
    "top_p": None,
    "num_beams": 1,
    "max_new_tokens": 512
})()

eval_model(args)

Install

git clone https://github.com/InternLM/InternLM-XComposer --depth=1
cd projects/ShareGPT4V
conda create -n share4v python=3.10 -y
conda activate share4v

pip install --upgrade pip
pip install -e .
pip install -e ".[train]"
pip install flash-attn --no-build-isolation

Demo

You can build your local demo by:

# run script
python tools/app.py

Data Preparation

You should follow this instruction Data.md to manage the datasets. Currently, we provide direct download access to the web data. However, to avoid potential disputes, we plan to release URLs for these datasets rather than the raw data in the near future.

Train

ShareGPT4V model training consists of two stages: (1) feature alignment stage: use our ShareGPT4V-PT dataset with 1.2M ShareCaptioner-generated high-quality image-text pairs to finetune the vision encoder, projector, and the LLM to align the textual and visual modalities; (2) visual instruction tuning stage: finetune the projector and LLM to teach the model to follow multimodal instructions.

To train on fewer GPUs, you can reduce the per_device_train_batch_size and increase the gradient_accumulation_steps accordingly. Always keep the global batch size the same: per_device_train_batch_size x gradient_accumulation_steps x num_gpus.

Hyperparameters

We use a similar set of hyperparameters as ShareGPT4V-7B in finetuning. Both hyperparameters used in pretraining and finetuning are provided below.

1. Pretraining

Hyperparameter	Global Batch Size	Learning rate	Epochs	Max length	Weight decay
ShareGPT4V-7B	256	2e-5	1	2048	0

2. Finetuning

Hyperparameter	Global Batch Size	Learning rate	Epochs	Max length	Weight decay
ShareGPT4V-7B	128	2e-5	1	2048	0

Pretrain

First, you should download the MLP projector pretrained by LLaVA-1.5 with LAION-CC-SBU-558K. Because a rough modality alignment process is beneficial before using high quality detailed captions for modality alignment.

You can run projects/ShareGPT4V/scripts/sharegpt4v/slurm_pretrain_7b.sh to pretrain the model. Remember to specify the projector path in the script. In this stage, we fine-tuned the second half of the vision encoder's blocks, projector, and LLM.

In our setup we used 16 A100 (80G) GPUs and the whole pre-training process lasted about 12 hours. You can adjust the number of gradient accumulation steps to reduce the number of GPUs.

Finetune

In this stage, we finetune the projector and LLM with sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json.

You can run projects/ShareGPT4V/scripts/sharegpt4v/slurm_finetune_7b.sh to finetune the model.

In our setup we used 16 A100 (80G) GPUs and the whole pre-training process lasted about 7 hours. You can adjust the number of gradient accumulation steps to reduce the number of GPUs.

Evaluation

To ensure the reproducibility, we evaluate the models with greedy decoding. We do not evaluate using beam search to make the inference process consistent with the chat demo of real-time outputs.

See Evaluation.md.

❤️ Acknowledgments

• LLaVA: the codebase we built upon. Thanks for their wonderful work.
• Vicuna: the amazing open-sourced large language model!

✒️ Citation

If you find our work helpful for your research, please consider giving a star ⭐ and citation 📝

@article{chen2023sharegpt4v,
  title={ShareGPT4V: Improving Large Multi-Modal Models with Better Captions},
  author={Chen, Lin and Li, Jisong and Dong, Xiaoyi and Zhang, Pan and He, Conghui and Wang, Jiaqi and Zhao, Feng and Lin, Dahua},
  journal={arXiv preprint arXiv:2311.12793},
  year={2023}
}

License

Usage and License Notices: The data and checkpoint is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of LLaMA, Vicuna and GPT-4. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.