Inquiry About Dataset, Model Architecture, and Experimental Procedures

[yihong1120]

Dear buiquangmanhhp1999, I hope this message finds you well. I have been exploring your fascinating project on License Plate Recognition and am impressed with its capabilities and design. However, I have a few questions regarding some specific aspects of your project, which I believe would greatly enhance my understanding and potentially contribute to its further development.

1. Dataset Details:

   • Could you provide more information about the dataset used for training and testing the models, particularly Yolo Tiny v3 and the CNN for character classification?
   • Are there any specific preprocessing steps or augmentations applied to the dataset?

2. Model Architecture:

   • Regarding the Yolo Tiny v3 model used for plate detection, could you share insights into any modifications or optimizations made to the standard architecture?
   • For the CNN used in character classification, I'm curious about the layer configurations, activation functions, and any unique features or techniques employed.

3. Experimental Methods:

   • Could you elaborate on the training procedure, including details like loss functions, optimizers, learning rate schedules, and any regularization techniques used?
   • How do you handle the challenges of plate recognition in diverse conditions, such as varying angles, lighting, or partial occlusions?

I believe these details will not only clarify the workings of your project but also provide valuable insights for those looking to contribute or learn from your work.

Thank you for your time and effort in developing this project. I look forward to your response and any additional information you can provide.

Best regards, yihong1120