|
--- |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: trocr-base-printed_license_plates_ocr |
|
results: [] |
|
language: |
|
- en |
|
metrics: |
|
- cer |
|
pipeline_tag: image-to-text |
|
--- |
|
|
|
# trocr-base-printed_license_plates_ocr |
|
|
|
This model is a fine-tuned version of [microsoft/trocr-base-printed](https://huggingface.co/microsoft/trocr-base-printed). |
|
|
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1581 |
|
- CER: 0.0368 |
|
|
|
## Model description |
|
|
|
This model extracts text from image input (License Plates). |
|
|
|
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)/OCR%20License%20Plates/OCR_license_plate_text_recognition.ipynb |
|
|
|
## Intended uses & limitations |
|
|
|
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. |
|
|
|
## Training and evaluation data |
|
|
|
Dataset Source: https://www.kaggle.com/datasets/nickyazdani/license-plate-text-recognition-dataset |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 5e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 2 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | CER | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| 0.3144 | 1.0 | 2000 | 0.2463 | 0.0473 | |
|
| 0.143 | 2.0 | 4000 | 0.1581 | 0.0368 | |
|
|
|
### Framework versions |
|
|
|
- Transformers 4.21.3 |
|
- Pytorch 1.12.1 |
|
- Datasets 2.4.0 |
|
- Tokenizers 0.12.1 |
