---
tags:
- ocr
- vision
---
**Note:** ORIGINAL MODEL REPO: https://github.com/Ucas-HaoranWei/GOT-OCR2.0
---
[Haoran Wei*](https://scholar.google.com/citations?user=J4naK0MAAAAJ&hl=en), Chenglong Liu*, Jinyue Chen, Jia Wang, Lingyu Kong, Yanming Xu, [Zheng Ge](https://joker316701882.github.io/), Liang Zhao, [Jianjian Sun](https://scholar.google.com/citations?user=MVZrGkYAAAAJ&hl=en), [Yuang Peng](https://scholar.google.com.hk/citations?user=J0ko04IAAAAJ&hl=zh-CN&oi=ao), Chunrui Han, [Xiangyu Zhang](https://scholar.google.com/citations?user=yuB-cfoAAAAJ&hl=en)
## Release
- [2024/9/03]🔥🔥🔥 We open-source the codes, weights, and benchmarks. The paper can be found in this [repo](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/blob/main/GOT-OCR-2.0-paper.pdf). We also have submitted it to Arxiv.
- [2024/9/03]🔥🔥🔥 We release the OCR-2.0 model GOT!
[](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 Vary.
## Community contributions
We encourage everyone to develop GOT applications based on this repo. Thanks for the following contributions :
[Colab of GOT](https://colab.research.google.com/drive/1nmiNciZ5ugQVp4rFbL9ZWpEPd92Y9o7p?usp=sharing) ~ contributor： [@Zizhe Wang](https://github.com/PaperPlaneDeemo)
## Contents
- [Install](#install)
- [GOT Weights](#got-weights)
- [Demo](#demo)
- [Train](#train)
- [Eval](#eval)
***
Towards OCR-2.0 via a Unified End-to-end Model
***
## Install
0. Our environment is cuda11.8+torch2.0.1
1. Clone this repository and navigate to the GOT folder
```bash
git clone https://github.com/Ucas-HaoranWei/GOT-OCR2.0.git
cd 'the GOT folder'
```
2. Install Package
```Shell
conda create -n got python=3.10 -y
conda activate got
pip install -e .
```
3. Install Flash-Attention
```
pip install ninja
pip install flash-attn --no-build-isolation
```
## GOT Weights
- [Google Drive](https://drive.google.com/drive/folders/1OdDtsJ8bFJYlNUzCQG4hRkUL6V-qBQaN?usp=sharing)
- [BaiduYun](https://pan.baidu.com/s/1G4aArpCOt6I_trHv_1SE2g) code: OCR2
## Demo
1. plain texts OCR:
```Shell
python3 GOT/demo/run_ocr_2.0.py --model-name /GOT_weights/ --image-file /an/image/file.png --type ocr
```
2. format texts OCR:
```Shell
python3 GOT/demo/run_ocr_2.0.py --model-name /GOT_weights/ --image-file /an/image/file.png --type format
```
3. fine-grained OCR:
```Shell
python3 GOT/demo/run_ocr_2.0.py --model-name /GOT_weights/ --image-file /an/image/file.png --type format/ocr --box [x1,y1,x2,y2]
```
```Shell
python3 GOT/demo/run_ocr_2.0.py --model-name /GOT_weights/ --image-file /an/image/file.png --type format/ocr --color red/green/blue
```
4. multi-crop OCR:
```Shell
python3 GOT/demo/run_ocr_2.0_crop.py --model-name /GOT_weights/ --image-file /an/image/file.png
```
5. multi-page OCR (the image path contains multiple .png files):
```Shell
python3 GOT/demo/run_ocr_2.0_crop.py --model-name /GOT_weights/ --image-file /images/path/ --multi-page
```
6. render the formatted OCR results:
```Shell
python3 GOT/demo/run_ocr_2.0.py --model-name /GOT_weights/ --image-file /an/image/file.png --type format --render
```
**Note**:
The rendering results can be found in /results/demo.html. Please open the demo.html to see the results.
## Train
1. This codebase only supports post-training (stage-2/stage-3) upon our GOT weights.
2. If you want train from stage-1 described in our paper, you need this [repo](https://github.com/Ucas-HaoranWei/Vary-tiny-600k).
```Shell
deepspeed /GOT-OCR-2.0-master/GOT/train/train_GOT.py \
--deepspeed /GOT-OCR-2.0-master/zero_config/zero2.json --model_name_or_path /GOT_weights/ \
--use_im_start_end True \
--bf16 True \
--gradient_accumulation_steps 2 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 200 \
--save_total_limit 1 \
--weight_decay 0. \
--warmup_ratio 0.001 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 8192 \
--gradient_checkpointing True \
--dataloader_num_workers 8 \
--report_to none \
--per_device_train_batch_size 2 \
--num_train_epochs 1 \
--learning_rate 2e-5 \
--datasets pdf-ocr+scence \
--output_dir /your/output.path
```
**Note**:
1. Change the corresponding data information in constant.py.
2. Change line 37 in conversation_dataset_qwen.py to your data_name.
## Eval
1. We use the [Fox](https://github.com/ucaslcl/Fox) and [OneChart](https://github.com/LingyvKong/OneChart) benchmarks, and other benchmarks can be found in the weights download link.
2. The eval codes can be found in GOT/eval.
3. You can use the evaluate_GOT.py to run the eval. If you have 8 GPUs， the --num-chunks can be set to 8.
```Shell
python3 GOT/eval/evaluate_GOT.py --model-name /GOT_weights/ --gtfile_path xxxx.json --image_path /image/path/ --out_path /data/eval_results/GOT_mathpix_test/ --num-chunks 8 --datatype OCR
```
## Contact
If you are interested in this work or have questions about the code or the paper, please join our communication [Wechat]() group.
## Acknowledgement
- [Vary](https://github.com/Ucas-HaoranWei/Vary/): 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
```bibtex
@article{wei2024general,
title={General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model},
author={Wei, Haoran and Liu, Chenglong and Chen, Jinyue and Wang, Jia and Kong, Lingyu and Xu, Yanming and Ge, Zheng and Zhao, Liang and Sun, Jianjian and Peng, Yuang and others},
journal={arXiv preprint arXiv:2409.01704},
year={2024}
}
@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}
} 