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--- |
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language: |
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- en |
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tags: |
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- generated_from_trainer |
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metrics: |
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- cer |
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pipeline_tag: image-to-text |
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base_model: microsoft/trocr-base-printed |
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model-index: |
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- name: trocr-base-printed_captcha_ocr |
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results: [] |
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--- |
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# trocr-base-printed_captcha_ocr |
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This model is a fine-tuned version of [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1380 |
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- Cer: 0.0075 |
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## Model description |
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This model extracts text from image Captcha inputs. |
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For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Optical%20Character%20Recognition%20(OCR)/Captcha/OCR_captcha.ipynb |
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## Intended uses & limitations |
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This model is intended to demonstrate my ability to solve a complex problem using technology. You are welcome to test and experiment with this model, but it is at your own risk/peril. |
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## Training and evaluation data |
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Dataset Source: https://www.kaggle.com/datasets/alizahidraja/captcha-data |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Cer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 10.4464 | 1.0 | 107 | 0.5615 | 0.0879 | |
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| 10.4464 | 2.0 | 214 | 0.2432 | 0.0262 | |
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| 10.4464 | 3.0 | 321 | 0.1380 | 0.0075 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
