DeepSeek-OCR/README.md

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<p align="center">
  <a href="https://huggingface.co/deepseek-ai/DeepSeek-OCR"><b>📥 Model Download</b></a> |
  <a href="https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSeek_OCR_paper.pdf"><b>📄 Paper Link</b></a> |
  <a href="./DeepSeek_OCR_paper.pdf"><b>📄 Arxiv Paper Link</b></a> |
</p>

<h2>
<p align="center">
  <a href="">DeepSeek-OCR: Contexts Optical Compression</a>
</p>
</h2>

<p align="center">
<img src="assets/fig1.png" style="width: 1000px" align=center>
</p>
<p align="center">
<a href="">Explore the boundaries of visual-text compression.</a>       
</p>

## Release
- [2025/x/x]🚀🚀🚀 We release DeepSeek-OCR, a model to investigate the role of vision encoders from an LLM-centric viewpoint.

## Contents
- [Install](#install)
- [vLLM Inference](#vllm-inference)
- [Transformers Inference](#transformers-inference)
  




## Install
>Our environment is cuda11.8+torch2.6.0.
1. Clone this repository and navigate to the DeepSeek-OCR folder
```bash
git clone https://github.com/deepseek-ai/DeepSeek-OCR.git
```
2. Conda
```Shell
conda create -n deepseek-ocr python=3.12.9 -y
conda activate deepseek-ocr
```
3. Packages

- download the vllm-0.8.5 [whl](https://github.com/vllm-project/vllm/releases/tag/v0.8.5) 
```Shell
pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu118
pip install vllm-0.8.5+cu118-cp38-abi3-manylinux1_x86_64.whl
pip install -r requirements.txt
pip install flash-attn==2.7.3 --no-build-isolation
```
**Note:** if you want vLLM and transformers codes to run in the same environment, you don't need to worry about this installation error like: vllm 0.8.5+cu118 requires transformers>=4.51.1

## vLLM-Inference
- VLLM:
>**Note:** change the INPUT_PATH/OUTPUT_PATH and other settings in the DeepSeek-OCR-master/DeepSeek-OCR-vllm/config.py
```Shell
cd DeepSeek-OCR-master/DeepSeek-OCR-vllm
```
1. image: streaming output
```Shell
python run_dpsk_ocr_image.py
```
2. pdf: concurrency ~2500tokens/s(an A100-40G)
```Shell
python run_dpsk_ocr_pdf.py
```
3. batch eval for benchmarks
```Shell
python run_dpsk_ocr_eval_batch.py
```
## Transformers-Inference
- Transformers
```python
from transformers import AutoModel, AutoTokenizer
import torch
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
model_name = 'deepseek-ai/DeepSeek-OCR'

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, _attn_implementation='flash_attention_2', trust_remote_code=True, use_safetensors=True)
model = model.eval().cuda().to(torch.bfloat16)

# prompt = "<image>\nFree OCR. "
prompt = "<image>\n<|grounding|>Convert the document to markdown. "
image_file = 'your_image.jpg'
output_path = 'your/output/dir'

res = model.infer(tokenizer, prompt=prompt, image_file=image_file, output_path = output_path, base_size = 1024, image_size = 640, crop_mode=True, save_results = True, test_compress = True)
```
or you can
```Shell
cd DeepSeek-OCR-master/DeepSeek-OCR-hf
python run_dpsk_ocr.py
```
## Support-Modes
The current open-source model supports the following modes:
- Native resolution:
  - Tiny: 512×512 （64 vision tokens）✅
  - Small: 640×640 （100 vision tokens）✅
  - Base: 1024×1024 （256 vision tokens）✅
  - Large: 1280×1280 （400 vision tokens）✅
- Dynamic resolution
  - Gundam: n×640×640 + 1×1024×1024 ✅

## Prompts examples
```python
# document: <image>\n<|grounding|>Convert the document to markdown.
# other image: <image>\n<|grounding|>OCR this image.
# without layouts: <image>\nFree OCR.
# figures in document: <image>\nParse the figure.
# general: <image>\nDescribe this image in detail.
# rec: <image>\nLocate <|ref|>xxxx<|/ref|> in the image.
# '先天下之忧而忧'
```


## Visualizations
<table>
<tr>
<td><img src="assets/show1.jpg" style="width: 500px"></td>
<td><img src="assets/show2.jpg" style="width: 500px"></td>
</tr>
<tr>
<td><img src="assets/show3.jpg" style="width: 500px"></td>
<td><img src="assets/show4.jpg" style="width: 500px"></td>
</tr>
</table>


## Acknowledgement

We would like to thank [Vary](https://github.com/Ucas-HaoranWei/Vary/), [GOT-OCR2.0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0/), [MinerU](https://github.com/opendatalab/MinerU), [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR), [OneChart](https://github.com/LingyvKong/OneChart), [Slow Perception](https://github.com/Ucas-HaoranWei/Slow-Perception) for their valuable models and ideas.

We also appreciate the benchmarks: [Fox](https://github.com/ucaslcl/Fox), [OminiDocBench](https://github.com/opendatalab/OmniDocBench).

## Citation

coming soon！