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--- |
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license: apache-2.0 |
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language: |
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- ko |
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metrics: |
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- cer |
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- wer |
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pipeline_tag: image-to-text |
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--- |
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# trOCR-final |
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fine-tuned for VisionEncoderDecoderModel(encoder , decoder) |
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encoder = 'facebook/deit-base-distilled-patch16-384' |
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decoder = 'klue/roberta-base' |
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## How to Get Started with the Model |
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```python |
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from transformers import VisionEncoderDecoderModel,AutoTokenizer, TrOCRProcessor |
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import torch |
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from PIL import Image |
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device = torch.device('cuda') # change 'cuda' if you need. |
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image_path='(your image path)' |
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image = Image.open(image_path) |
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#model can be .jpg or .png |
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#hugging face download: https://huggingface.co/gg4ever/trOCR-final |
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processor = TrOCRProcessor.from_pretrained("microsoft/trocr-base-handwritten") |
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trocr_model = "gg4ever/trOCR-final" |
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model = VisionEncoderDecoderModel.from_pretrained(trocr_model).to(device) |
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tokenizer = AutoTokenizer.from_pretrained(trocr_model) |
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pixel_values = (processor(image, return_tensors="pt").pixel_values).to(device) |
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generated_ids = model.generate(pixel_values) |
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generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(generated_text) |
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``` |
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## Training Details |
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### Training Data |
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1M words generated by TextRecognitionDataGenerator(trdg) : https://github.com/Belval/TextRecognitionDataGenerator/blob/master/trdg/run.py |
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1.1M words from AI-hub OCR words dataset : https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&dataSetSn=81 |
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### Training Hyperparameters |
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|hyperparameters|values| |
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|-----------------------------|-------| |
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|predict_with_generate|True| |
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|evaluation_strategy|"steps"| |
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|per_device_train_batch_size|32| |
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|per_device_eval_batch_size|32| |
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|num_train_epochs|2| |
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|fp16|True| |
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|learning_rate|4e-5| |
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|eval_stept|10000| |
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|warmup_steps|20000| |
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|weight_decay|0.01| |
