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--- |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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language: |
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- multilingual |
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tags: |
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- got |
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- vision-language |
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- ocr2.0 |
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- custom_code |
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license: apache-2.0 |
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--- |
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## Usage |
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Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10： |
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``` |
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torch==2.0.1 |
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torchvision==0.15.2 |
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transformers==4.37.2 |
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tiktoken==0.6.0 |
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verovio==4.3.1 |
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accelerate==0.28.0 |
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``` |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained('Cherryblade29/ocr', trust_remote_code=True) |
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model = AutoModel.from_pretrained('Cherryblade29/ocr', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id) |
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model = model.eval().cuda() |
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# input your test image |
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image_file = 'xxx.jpg' |
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# plain texts OCR |
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res = model.chat(tokenizer, image_file, ocr_type='ocr') |
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# format texts OCR: |
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# res = model.chat(tokenizer, image_file, ocr_type='format') |
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# fine-grained OCR: |
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# res = model.chat(tokenizer, image_file, ocr_type='ocr', ocr_box='') |
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# res = model.chat(tokenizer, image_file, ocr_type='format', ocr_box='') |
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# res = model.chat(tokenizer, image_file, ocr_type='ocr', ocr_color='') |
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# res = model.chat(tokenizer, image_file, ocr_type='format', ocr_color='') |
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# multi-crop OCR: |
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# res = model.chat_crop(tokenizer, image_file, ocr_type='ocr') |
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# res = model.chat_crop(tokenizer, image_file, ocr_type='format') |
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# render the formatted OCR results: |
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# res = model.chat(tokenizer, image_file, ocr_type='format', render=True, save_render_file = './demo.html') |
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print(res) |
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``` |
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