Instructions to use zenosai/MonkeyOCRv2-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenosai/MonkeyOCRv2-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="zenosai/MonkeyOCRv2-B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zenosai/MonkeyOCRv2-B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update pipeline tag to image-feature-extraction and add library_name
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by nielsr HF Staff - opened
README.md
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license: apache-2.0
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datasets:
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- zenosai/MonkeyDocv2
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<div align="center" xmlns="http://www.w3.org/1999/html">
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<h2>
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<b>MonkeyOCRv2: A Visual-Text Foundation Model for Document AI</b>
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#### 3. Text detection results on Total-Text, CTW1500, ICDAR2015 and ArT. We follow the training and evaluation protocols of [MMOCR](https://github.com/open-mmlab/mmocr) and [DPText-DETR](https://github.com/ymy-k/DPText-DETR).
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<p align="center">
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<img src="https://raw.githubusercontent.com/Yuliang-Liu/MonkeyOCRv2/refs/heads/main/asserts/overview.png
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</p>
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<table>
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<thead>
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<tr>
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<th
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<th
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<th style="text-align: center;" colspan="2">Overall</th>
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<th style="text-align: center;" colspan="4">DocTamper-Test</th>
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<th style="text-align: center;" colspan="4">DocTamper-FCD</th>
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<th style="text-align: center;" colspan="4">DocTamper-SCD</th>
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</tr>
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<tr>
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<th style="text-align: center;">IoU</th>
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<th style="text-align: center;">F</th>
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<th style="text-align: center;">IoU</th>
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- **Qwen3**: https://github.com/QwenLM/Qwen3
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## Copyright
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We warmly welcome your feedback, suggestions, and contributions, which are essential to the continued development and improvement of our framework. Note: This model is intended for academic research and non-commercial use only. For any questions, please contact us at xbai@hust.edu.cn or ylliu@hust.edu.cn.
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---
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datasets:
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- zenosai/MonkeyDocv2
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license: apache-2.0
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pipeline_tag: image-feature-extraction
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library_name: transformers
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---
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<div align="center" xmlns="http://www.w3.org/1999/html">
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<h2>
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<b>MonkeyOCRv2: A Visual-Text Foundation Model for Document AI</b>
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#### 3. Text detection results on Total-Text, CTW1500, ICDAR2015 and ArT. We follow the training and evaluation protocols of [MMOCR](https://github.com/open-mmlab/mmocr) and [DPText-DETR](https://github.com/ymy-k/DPText-DETR).
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<p align="center">
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<img src="https://raw.githubusercontent.com/Yuliang-Liu/MonkeyOCRv2/refs/heads/main/asserts/overview.png" width="600"/>
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</p>
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<table>
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<thead>
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<tr>
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<th>Method</th>
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<th>Params</th>
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<th style="text-align: center;" colspan="2">Overall</th>
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<th style="text-align: center;" colspan="4">DocTamper-Test</th>
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<th style="text-align: center;" colspan="4">DocTamper-FCD</th>
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<th style="text-align: center;" colspan="4">DocTamper-SCD</th>
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</tr>
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<tr>
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<th rowspan="2"></th>
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<th rowspan="2"></th>
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<th style="text-align: center;">IoU</th>
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<th style="text-align: center;">F</th>
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<th style="text-align: center;">IoU</th>
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- **Qwen3**: https://github.com/QwenLM/Qwen3
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## Copyright
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We warmly welcome your feedback, suggestions, and contributions, which are essential to the continued development and improvement of our framework. Note: This model is intended for academic research and non-commercial use only. For any questions, please contact us at xbai@hust.edu.cn or ylliu@hust.edu.cn.
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