tensoic-ai / tinyllavabench Goto Github PK

• [2024.02.25] Update evaluation scripts and docs!
• [2024.02.25] Data descriptions out. Release TinyLLaVA-1.5B and TinyLLaVA-2.0B!
• [2024.02.24] Example code on inference and model loading added!
• [2024.02.23] Evaluation code and scripts released!
• [2024.02.21] Creating the TinyLLaVABench repository on GitHub!
• [2024.02.21] Our paper: TinyLLaVA: A Framework of Small-scale Large Multimodal Models is out!
• [2024.01.11] Our fist model TinyLLaVA-1.4B is out!

• Add support for Ollama and llama.cpp.
• Developers' guide / How to build demo locally.
• Model Zoo descriptions.
• Examples and inference.
• Release code for training.
• Add descriptions for evaluation.
• Add descriptions for data preparation.
• Release TinyLLaVA-1.5B and TinyLLaVA-2.0B.
• Release TinyLLaVA-3.1B.
• Release the evaluation code and weights today(2024.2.23).

• Our best model, TinyLLaVA-3.1B, achieves better overall performance against existing 7B models such as LLaVA-1.5 and Qwen-VL.

https://huggingface.co/bczhou/tiny-llava-v1-hf

• TinyLLaVA-3.1B
• TinyLLaVA-2.0B
• TinyLLaVA-1.5B

We recommend the requirements as follows.

1. Clone this repository and navigate to LLaVA folder

git clone https://github.com/DLCV-BUAA/TinyLLaVABench.git
cd TinyLLaVABench

2. Install Package

conda create -n tinyllava python=3.10 -y
conda activate tinyllava
pip install --upgrade pip  # enable PEP 660 support
pip install -e .

3. Install additional packages for training cases

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

git pull
pip install -e .

# if you see some import errors when you upgrade, please try running the command below (without #)
# pip install flash-attn --no-build-isolation --no-cache-dir

from tinyllava.model.builder import load_pretrained_model
from tinyllava.mm_utils import get_model_name_from_path
from tinyllava.eval.run_tiny_llava import eval_model

model_path = "bczhou/TinyLLaVA-3.1B"

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)
)

from tinyllava.model.builder import load_pretrained_model
from tinyllava.mm_utils import get_model_name_from_path
from tinyllava.eval.run_tiny_llava import eval_model

model_path = "bczhou/TinyLLaVA-3.1B"
prompt = "What are the things I should be cautious about when I visit here?"
image_file = "https://llava-vl.github.io/static/images/view.jpg"

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

eval_model(args)

To ensure the reproducibility, we evaluate the models with greedy decoding.

See Evaluation.md

In our paper, we used two different datasets: the LLaVA dataset and the ShareGPT4V dataset, and compared their differences. In this section, we provide information on data preparation.

• LLaVA: The pretraining images of LLaVA is from the 558K subset of the LAION-CC-SBU dataset.
• ShareGPT4V: The pretraining images of ShareGPT4V is a mixture of 558K LAION-CC-SBU subset, SAM dataset, and COCO dataset.

• LLaVA: The pretraining annotations of LLaVA are here.
• ShareGPT4V: The pretraining annotations of ShareGPT4V are here.

The majority of the two SFT datasets are the same, with the exception that the 23K detailed description data in LLaVA-1.5-SFT being replaced with detailed captions randomly sampled from the 100K ShareGPT4V data.

1. Download relevant images

• LAION-CC-SBU-558K: images.zip
• COCO: This dataset is from the COCO2017 challenge. Download: train2017
• WebData: This dataset is curated by the ShareGPT4V project. Download: images. Only for academic usage.
• SAM: This dataset is collected by Meta. Download: images. We only use 000000~000050.tar for now. If you just want to use ShareGPT4V for SFT, you can quickly download 9K images from here.
• GQA: GQA project page. Download: images
• OCR-VQA: OCR-VQA project page. Download: download script. We save all files as .jpg
• TextVQA: TextVQA project page. Download: trainvalimages
• VisualGenome: VisualGenome project page. Download: part1, part2

2. Download relevant annotations

• LLaVA's pretraining annotations: blip_laion_cc_sbu_558k.json
• LLaVA's SFT annotations: llava_v1_5_mix665k.json
• ShareGPT4V's pretraining annotations: share-captioner_coco_lcs_sam_1246k_1107.json
• ShareGPT4V's SFT annotations: sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json

Organize the image files and annotation files as follows in path/to/your/data:

data
├── llava
│   ├── llava_pretrain
│   │   ├── images
│   │   ├── blip_laion_cc_sbu_558k.json
├── coco
│   ├── train2017
├── sam
│   ├── images
├── gqa
│   ├── images
├── ocr_vqa
│   ├── images
├── textvqa
│   ├── train_images
├── vg
│   ├── VG_100K
│   ├── VG_100K_2
├── share_textvqa
│   ├── images
├── web-celebrity
│   ├── images
├── web-landmark
│   ├── images
├── wikiart
│   ├── images
├── text_files
│   ├── llava_v1_5_mix665k.json
│   ├── share-captioner_coco_lcs_sam_1246k_1107.json
│   ├── sharegpt4v_mix665k_cap23k_coco-ap9k_lcs3k_sam9k_div2k.json

If you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝.

@misc{zhou2024tinyllava,
      title={TinyLLaVA: A Framework of Small-scale Large Multimodal Models}, 
      author={Baichuan Zhou and Ying Hu and Xi Weng and Junlong Jia and Jie Luo and Xien Liu and Ji Wu and Lei Huang},
      year={2024},
      eprint={2402.14289},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

• Our codebase is built upon the LLaVA project. Great work!
• Our project uses data from the ShareGPT4V project. Great work!