[LEADERG AI ZOO] Jupyter-Image-LPRNet-PyTorch

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繁體中文

 

 

 

[Introduction]

 

 

Use LPRNet for license plate recognition.

 

 

 

[Instructions]

 

 

1. 1_train.ipynb

Perform training.

 

 

Set the parameters:

• learning_rate: learning rate

• train_img_dirs: folder of training images

• test_img_dirs: test image folder

• train_batch_size: training batch size

• test_batch_size: test batch size

• save_interval: save interval

• max_epoch: the maximum number of training epochs

• save_folder: the folder path where the model is saved

• pretrained_model: the file path of the pretrained model

 

 

After the setting is completed, it can be executed.

 

 

2. 2_inference.ipynb

Perform inference.

 

 

Set the parameters:

• test_img: test image file path

• pretrained_model: the file path of the pretrained model

 

 

After the setting is completed, it can be executed.

After execution, the result of the inference can be obtained.

 

 

3. 3_inference_folder.ipynb

Perform inference on folder files.

 

 

Set the parameters:

• test_img_dirs: test image folder path

• pretrained_model: the file path of the pretrained model

 

 

After the setting is completed, it can be executed.

After execution, the result of the inference can be obtained.

 

 

 

Jupyter-Image-LPRNet-PyTorch-inference.png

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