
# 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
{"language": ["en"], "tags": ["generated_from_trainer"], "metrics": ["cer"], "pipeline_tag": "image-to-text", "model-index": [{"name": "trocr-base-printed_license_plates_ocr", "results": []}]}
transformers pytorch vision-encoder-decoder generated_from_trainer image-to-text en endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #image-to-text #en #endpoints_compatible #has_space #region-us 

trocr-base-printed\_license\_plates\_ocr
========================================


This model is a fine-tuned version of 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: URL


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: URL


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



### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1



### 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 ### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #image-to-text #en #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #vision-encoder-decoder #generated_from_trainer #image-to-text #en #endpoints_compatible #has_space #region-us 
### 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### Framework versions


* Transformers 4.21.3
* Pytorch 1.12.1
* Datasets 2.4.0
* Tokenizers 0.12.1