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