# GOT-OCR2.0
**Repository Path**: phpmvcv2/GOT-OCR2.0
## Basic Information
- **Project Name**: GOT-OCR2.0
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-10-14
- **Last Updated**: 2024-10-14
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[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
- [2024/10/11] Too many friends want to join the wechat group, so we created a fourth [group](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/blob/main/assets/wechat4.jpg).
- [2024/10/2] [onnx](https://github.com/BaofengZan/GOT-OCRv2-onnx) and [mnn](https://github.com/BaofengZan/mnn-llm-GOT-OCR2.0) versions of GOT-OCR2.0.
- [2024/9/29]π₯π₯π₯ The community has implemented the first version of [llama_cpp_inference](https://github.com/1694439208/GOT-OCR-Inference).
- [2024/9/24]π₯π₯π₯ Support [ms-swift](https://github.com/modelscope/ms-swift/issues/2122) quick [Fine-tune](#fine-tune) for your own data.
- [2024/9/23]π₯π₯π₯ We release the official [Modelscope demo](https://modelscope.cn/studios/stepfun-ai/GOT_official_online_demo). Thanks very much for Modelscope providing the GPU resource.
- [2024/9/14]π₯π₯π₯ We release the official [demo](https://huggingface.co/spaces/ucaslcl/GOT_online). Thanks very much for Huggingface providing the GPU resource.
- [2024/9/13]π₯π₯π₯ We release the [Huggingface](https://huggingface.co/ucaslcl/GOT-OCR2_0) deployment.
- [2024/9/03]π₯π₯π₯ 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]π₯π₯π₯ 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 :
[onnx and mnn supports](https://github.com/BaofengZan/GOT-OCRv2-onnx) ~ contributor: [@BaofengZan](https://github.com/BaofengZan)
[llama_cpp inference](https://github.com/1694439208/GOT-OCR-Inference) ~ contributor: [@1694439208](https://github.com/1694439208)
[Colab of GOT](https://colab.research.google.com/drive/1nmiNciZ5ugQVp4rFbL9ZWpEPd92Y9o7p?usp=sharing) ~ contributor: [@Zizhe Wang](https://github.com/PaperPlaneDeemo)
[CPU version of GOT](https://github.com/ElvisClaros/GOT-OCR2.0) ~ contributor: [@ElvisClaros](https://github.com/ElvisClaros)
[Online demo](https://huggingface.co/spaces/Tonic/GOT-OCR) ~ contributor: [@Joseph Pollack](https://huggingface.co/Tonic)
[Dokcer & client demo](https://github.com/QIN2DIM/GOT-OCR2.0) ~ contributor: [@QIN2DIM](https://github.com/QIN2DIM)
[GUI of GOT](https://github.com/XJF2332/GOT-OCR-2-GUI) ~ contributor: [@XJF2332](https://github.com/XJF2332)
## Contents
- [Install](#install)
- [GOT Weights](#got-weights)
- [Demo](#demo)
- [Train](#train)
- [Fine-tune](#fine-tune)
- [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
- [Huggingface](https://huggingface.co/ucaslcl/GOT-OCR2_0)
- [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
0. Train sample can be found [here](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/blob/main/assets/train_sample.jpg). Note that the '\' in the 'conversations'-'human'-'value' is necessary!
1. This codebase only supports post-training (stage-2/stage-3) upon our GOT weights.
2. If you want to 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](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/tree/main/GOT-OCR-2.0-master/GOT/utils).
2. Change line 37 in [conversation_dataset_qwen.py](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/tree/main/GOT-OCR-2.0-master/GOT/data) to your data_name.
## Fine-tune
Quick Fine-tune with ms-swift:
```Shell
git clone https://github.com/modelscope/ms-swift.git
cd ms-swift
pip install -e .[llm]
```
```Shell
# defaultοΌsft LLM & projector, freeze vision encoder
CUDA_VISIBLE_DEVICES=0 swift sft\
--model_type got-ocr2 \
--model_id_or_path stepfun-ai/GOT-OCR2_0 \
--sft_type lora \
--dataset latex-ocr-print#5000
# Deepspeed ZeRO2
NPROC_PER_NODE=4 \
CUDA_VISIBLE_DEVICES=0,1,2,3 swift sft \
--model_type got-ocr2 \
--model_id_or_path stepfun-ai/GOT-OCR2_0 \
--sft_type lora \
--dataset latex-ocr-print#5000 \
--deepspeed default-zero2
```
**With your data**:
```Shell
--dataset train.jsonl
--val_dataset val.jsonl (optional)
```
**Data format**:
```Shell
{"query": "55555", "response": "66666", "images": ["image_path"]}
{"query": "eeeee", "response": "fffff", "history": [], "images": ["image_path1", "image_path2"]}
{"query": "EEEEE", "response": "FFFFF", "history": [["query1", "response1"], ["query2", "response2"]]}
```
More details can be seen in [ms-swift](https://github.com/modelscope/ms-swift/issues/2122).
## 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 GPUsοΌ 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](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/blob/main/assets/wechat.jpg) group.
**Note**:
All three wechat groups are full, please join [group 4](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/blob/main/assets/wechat4.jpg).
Don't hesitate to contact me by email, weihaoran18@mails.ucas.ac.cn, if you have any questions.
## 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}
} 
