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--- |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: image-text-to-text |
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tags: |
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- ocr |
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- vision-language |
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- qwen2-vl |
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- vila |
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- multimodal |
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license: apache-2.0 |
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--- |
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# Easy DeepOCR - VILA-Qwen2-VL-8B |
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A vision-language model fine-tuned for OCR tasks, based on VILA architecture with Qwen2-VL-8B as the language backbone. |
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## Model Description |
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This model combines: |
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- **Language Model**: Qwen2-VL-8B |
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- **Vision Encoders**: SAM + CLIP |
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- **Architecture**: VILA (Visual Language Adapter) |
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- **Task**: Optical Character Recognition (OCR) |
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## Model Structure |
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``` |
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easy_deepocr/ |
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├── config.json # Model configuration |
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├── llm/ # Qwen2-VL-8B language model weights |
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├── mm_projector/ # Multimodal projection layer |
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├── sam_clip_ckpt/ # SAM and CLIP vision encoder weights |
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└── trainer_state.json # Training state information |
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``` |
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## Usage |
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```python |
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# TODO: Add your inference code here |
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from transformers import AutoModel, AutoTokenizer |
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model = AutoModel.from_pretrained("pkulium/easy_deepocr", trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained("pkulium/easy_deepocr") |
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# Example inference |
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# image = ... |
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# text = ... |
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``` |
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## Training Details |
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- **Base Model**: Qwen2-VL-8B |
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- **Vision Encoders**: SAM + CLIP |
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- **Training Framework**: VILA |
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- **Training Type**: Pretraining for OCR tasks |
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## Intended Use |
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This model is designed for: |
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- Document OCR |
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- Scene text recognition |
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- Handwriting recognition |
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- Multi-language text extraction |
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## Limitations |
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- [Add any known limitations] |
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- Model performance may vary with image quality |
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- Best suited for [specify use cases] |
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## Citation |
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If you use this model, please cite: |
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```bibtex |
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@misc{easy_deepocr, |
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author = {Ming Liu}, |
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title = {Easy DeepOCR - VILA-Qwen2-VL-8B}, |
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year = {2025}, |
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publisher = {HuggingFace}, |
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url = {https://huggingface.co/pkulium/easy_deepocr} |
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} |
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``` |
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## Acknowledgments |
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- [VILA](https://github.com/NVlabs/VILA) for the architecture |
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- [Qwen2-VL](https://github.com/QwenLM/Qwen2-VL) for the language model |
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- SAM and CLIP for vision encoding capabilities |
