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https://www.modelscope.cn/deepseek-ai/DeepSeek-OCR.git
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123 lines
4.2 KiB
Markdown
123 lines
4.2 KiB
Markdown
---
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pipeline_tag: image-text-to-text
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language:
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- multilingual
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tags:
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- deepseek
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- vision-language
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- ocr
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- custom_code
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license: mit
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---
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<div align="center">
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<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek AI" />
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</div>
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<hr>
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<div align="center">
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<a href="https://www.deepseek.com/" target="_blank">
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<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" />
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</a>
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<a href="https://huggingface.co/deepseek-ai/DeepSeek-OCR" target="_blank">
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<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" />
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</a>
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</div>
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<div align="center">
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<a href="https://discord.gg/Tc7c45Zzu5" target="_blank">
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<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" />
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</a>
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<a href="https://twitter.com/deepseek_ai" target="_blank">
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<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" />
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</a>
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</div>
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<p align="center">
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<a href="https://github.com/deepseek-ai/DeepSeek-OCR"><b>🌟 Github</b></a> |
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<a href="https://huggingface.co/deepseek-ai/DeepSeek-OCR"><b>📥 Model Download</b></a> |
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<a href="https://github.com/deepseek-ai/DeepSeek-OCR/blob/main/DeepSeek_OCR_paper.pdf"><b>📄 Paper Link</b></a> |
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<a href=""><b>📄 Arxiv Paper Link</b></a> |
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</p>
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<h2>
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<p align="center">
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<a href="">DeepSeek-OCR: Contexts Optical Compression</a>
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</p>
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</h2>
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<p align="center">
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<img src="assets/fig1.png" style="width: 1000px" align=center>
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</p>
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<p align="center">
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<a href="">Explore the boundaries of visual-text compression.</a>
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</p>
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## Usage
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Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.12.9 + CUDA11.8：
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```
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torch==2.6.0
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transformers==4.46.3
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tokenizers==0.20.3
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einops
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addict
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easydict
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pip install flash-attn==2.7.3 --no-build-isolation
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```
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```python
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from transformers import AutoModel, AutoTokenizer
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import torch
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import os
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os.environ["CUDA_VISIBLE_DEVICES"] = '0'
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model_name = 'deepseek-ai/DeepSeek-OCR'
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModel.from_pretrained(model_name, _attn_implementation='flash_attention_2', trust_remote_code=True, use_safetensors=True)
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model = model.eval().cuda().to(torch.bfloat16)
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# prompt = "<image>\nFree OCR. "
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prompt = "<image>\n<|grounding|>Convert the document to markdown. "
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image_file = 'your_image.jpg'
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output_path = 'your/output/dir'
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# infer(self, tokenizer, prompt='', image_file='', output_path = ' ', base_size = 1024, image_size = 640, crop_mode = True, test_compress = False, save_results = False):
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# Tiny: base_size = 512, image_size = 512, crop_mode = False
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# Small: base_size = 640, image_size = 640, crop_mode = False
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# Base: base_size = 1024, image_size = 1024, crop_mode = False
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# Large: base_size = 1280, image_size = 1280, crop_mode = False
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# Gundam: base_size = 1024, image_size = 640, crop_mode = True
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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)
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```
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## vLLM
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Refer to [🌟GitHub](https://github.com/deepseek-ai/DeepSeek-OCR/) for guidance on model inference acceleration and PDF processing, etc.<!-- -->
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## Visualizations
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<table>
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<tr>
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<td><img src="assets/show1.jpg" style="width: 500px"></td>
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<td><img src="assets/show2.jpg" style="width: 500px"></td>
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</tr>
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<tr>
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<td><img src="assets/show3.jpg" style="width: 500px"></td>
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<td><img src="assets/show4.jpg" style="width: 500px"></td>
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</tr>
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</table>
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## Acknowledgement
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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.
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We also appreciate the benchmarks: [Fox](https://github.com/ucaslcl/Fox), [OminiDocBench](https://github.com/opendatalab/OmniDocBench).
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## Citation
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Coming soon!
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