LPRNet_Pytorch

To train the model, follow these steps:

1. Prepare Datasets: Ensure all images in your dataset are resized to exactly 94x24 pixels.
2. Configure Paths: Modify the train_LPRNet.py script to point to your dataset directory using the --train_img_dirs hyperparameter.
3. Adjust Hyperparameters: Modify other hyperparameters in the script as needed for your specific dataset.
4. Execute Training: Run the training script via the command line.

python train_LPRNet.py

To evaluate the model on a test dataset:

1. Prepare Datasets: Ensure all test images are resized to 94x24 pixels.
2. Configure Paths: Modify the test_LPRNet.py script to point to your test images using the --test_img_dirs hyperparameter.
3. Execute Testing: Run the testing script. To visualize the testing results, include the --show true or --show 1 flag.

python test_LPRNet.py --show true

To use LPRNet_Pytorch, ensure your environment meets the following requirements:

• pytorch >= 1.0.0
• opencv-python 3.x
• python 3.x
• imutils
• Pillow
• numpy