base on [ECCV 2024] Official code implementation of Vary: Scaling Up the Vision Vocabulary of Large Vision Language Models. <h3><a href="">Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models</a></h3>
<a href="https://varybase.github.io/"><img src="https://img.shields.io/badge/Project-Page-Green"></a>
<a href="https://arxiv.org/abs/2312.06109"><img src="https://img.shields.io/badge/Paper-PDF-orange"></a>
<a href="http://region-31.seetacloud.com:22701/"><img src="https://img.shields.io/badge/demo-blue"></a>
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[Haoran Wei*](https://scholar.google.com/citations?user=J4naK0MAAAAJ&hl=en), Lingyu Kong*, Jinyue Chen, Liang Zhao, [Zheng Ge](https://joker316701882.github.io/), [Jinrong Yang](https://yancie-yjr.github.io/), [Jianjian Sun](https://scholar.google.com/citations?user=MVZrGkYAAAAJ&hl=en), Chunrui Han, [Xiangyu Zhang](https://scholar.google.com/citations?user=yuB-cfoAAAAJ&hl=en)
<p align="center">
<img src="assets/logo.jpg" style="width: 200px" align=center>
</p>
## Release
- [2024/9/03] 🔥🔥🔥 We release a very strong and comprehensive OCR model [GOT-OCR2.0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0).
- [2024/7/16] 🎉🎉🎉 [OneChart](https://github.com/LingyvKong/OneChart) is accepted by ACM'MM 2024 oral (3.97%)!
- [2024/7/2] 🔥🔥🔥 Vary is accepted by ECCV2024. To thank everyone for their attention, I will release a model that performs on par with the Vary-document soon.
- [2024/5/27] 🔥🔥🔥 We present a document understanding benchmark in [Fox](https://github.com/ucaslcl/Fox) .
- [2024/5/24] 🔥🔥🔥 We propose a multi-page document understanding work -- [Fox](https://arxiv.org/abs/2405.14295), which supports 8-page pdf-image input !!!
- [2024/4/21] 🔥🔥🔥 For OneChart, we have released the web demo in [Project Page](https://onechartt.github.io/). Have fun!!
- [2024/4/21] 🔥🔥🔥 We present a Vary-tiny LAVIS codebase (for training from scratch) and the Vary-600k dataset (300K English and 300K Chinese pages) [here](https://github.com/Ucas-HaoranWei/Vary-tiny-600k) !!!
- [2024/4/15]🔥🔥🔥We release a chart parsing model OneChart [here](https://github.com/LingyvKong/OneChart).
- [2024/4/12]🔥🔥🔥We will release a chart parsing model based on Vary-tiny next week. The model supports both English and Chinese charts.
- [2024/3/16]🔥🔥🔥I found many friends very interested in Vary-tiny(OPT-125M), so I opened source it [here](https://huggingface.co/HaoranWei/Vary-tiny-opt125M/tree/main), a PDF-dense OCR and object detection version.
- [2023/1/23]🔥🔥🔥We release the Vary-toy [here](https://github.com/Ucas-HaoranWei/Vary-toy). Besides, we show the super good Vary-family results [here](https://github.com/Ucas-HaoranWei/Vary-family).
- [2023/12/29]🔥🔥🔥We will release a new model (a small-size Vary, about 2B) at the beginning of next month and introduce a new feature (object detection). Our online demo will be temporarily closed to prepare for the deployment of the new model.
- [2023/12/11] We released the online demo, have fun!
- [2023/12/11] We released the codes of Vary (train and inference)!
[](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE)
[](https://github.com/tatsu-lab/stanford_alpaca/blob/main/DATA_LICENSE)
**Usage and License Notices**: The data, code, and checkpoint are intended and licensed for research use only. They are also restricted to use that follow the license agreement of LLaMA, Vicuna, GPT-4, Qwen, and LLaVA.
## Contents
- [Install](#install)
- [Vary Weights](#vary-weights)
- [Demo](#Demo)
- [Train](#train)
## Install
1. Clone this repository and navigate to the Vary folder
```bash
git clone https://github.com/Ucas-HaoranWei/Vary.git
cd Vary
```
2. Install Package
```Shell
conda create -n vary python=3.10 -y
conda activate vary
pip install e .
```
3. Install Flash-Attention
```
pip install ninja
pip install flash-attn --no-build-isolation
```
## Vary Weights
- If you are in urgent need of weights for your research recently, please contact me by email.
- Download the CLIP-VIT-L in [Hugging Face](https://huggingface.co/openai/clip-vit-large-patch14/tree/main)
## Demo
1. Update the CLIP-VIT path in the codes (/cache/vit-large-patch14/) to your path.
2.
```Shell
python vary/demo/run_qwen_vary.py --model-name /vary/model/path/ --image-file /an/image/file.png
```
## Train
- We currently do not plan to open source the weights of the intermediate.
- However, we release the train codes. So you can train on your own dataset.
If you want to do this, you can try this:
1. For Vary-base (one machine, if you have multiple machines you need to prepare your host file)
```Shell
deepspeed Vary/train/train_qwen_vary.py --deepspeed /Vary/zero_config/zero2.json
--model_name_or_path /Qwen-7B/path/
--vision_tower /vit-large-patch14/path/
--freeze_vision_tower True
--freeze_lm_model False
--vision_select_layer -2
--use_im_start_end True
--bf16 True
--per_device_eval_batch_size 4
--gradient_accumulation_steps 1
--evaluation_strategy "no"
--save_strategy "steps"
--save_steps 5000
--save_total_limit 1
--weight_decay 0.
--warmup_ratio 0.03
--lr_scheduler_type "cosine"
--logging_steps 1 --tf32 True
--model_max_length 4096
--gradient_checkpointing True
--dataloader_num_workers 4
--report_to none
--per_device_train_batch_size 4
--num_train_epochs 1
--learning_rate 5e-5
--datasets data_name1+data_name2+data_name3
--output_dir /path/to/output/
```
2. For Vary-tiny
```Shell
deepspeed Vary/train/train_opt.py --deepspeed /Vary/zero_config/zero2.json
--model_name_or_path /opt125m/path/
--conversation_version opt
--freeze_vision_tower False
--freeze_lm_model False
--use_im_start_end True
--bf16 True
--per_device_eval_batch_size 4
--gradient_accumulation_steps 1
--evaluation_strategy "no"
--save_strategy "steps"
--save_steps 5000
--save_total_limit 1
--weight_decay 0.
--warmup_ratio 0.03
--lr_scheduler_type "cosine"
--logging_steps 1 --tf32 True
--model_max_length 4096
--gradient_checkpointing True
--dataloader_num_workers 4
--report_to none
--per_device_train_batch_size 16
--num_train_epochs 1
--learning_rate 5e-5
--datasets data_name1+data_name2+data_name3
--output_dir /path/to/output/
```
## Contact
If you have any questions related to the code or the paper, feel free to email (`
[email protected]`).
## Acknowledgement
- [LLaVA](https://github.com/lm-sys/FastChat): the codebase we built upon!
- [Qwen](https://github.com/QwenLM/Qwen): the LLM base model of Vary, which is good at both English and Chinese!
## Citation
If you find our work useful in your research, please consider citing Vary:
```bibtex
@article{wei2023vary,
title={Vary: Scaling up the Vision Vocabulary for Large Vision-Language Models},
author={Wei, Haoran and Kong, Lingyu and Chen, Jinyue and Zhao, Liang and Ge, Zheng and Yang, Jinrong and Sun, Jianjian and Han, Chunrui and Zhang, Xiangyu},
journal={arXiv preprint arXiv:2312.06109},
year={2023}
}
@article{wei2024small,
title={Small Language Model Meets with Reinforced Vision Vocabulary},
author={Wei, Haoran and Kong, Lingyu and Chen, Jinyue and Zhao, Liang and Ge, Zheng and Yu, En and Sun, Jianjian and Han, Chunrui and Zhang, Xiangyu},
journal={arXiv preprint arXiv:2401.12503},
year={2024}
}
```
", Assign "at most 3 tags" to the expected json: {"id":"5978","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"