Optimizing Training Parameters for Enhanced Performance on YOLOv8m Model with Large License Plate Character Dataset

[newegy]

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Question

How can I optimize the training parameters for improved performance on the YOLOv8m model with a dataset of 25,000 photos, 45 classes, and approximately 250,000 annotations? The dataset focuses on characters of car license plates, but despite its size, the accuracy achieved during training is similar to that of a smaller dataset of 2,000 images. Specifically, what are the recommended values for parameters such as the initial learning rate (lr0), final learning rate (lrf), and any other relevant parameters that can enhance the model's performance?

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[glenn-jocher]

@newegy hyperparameter tuning for deep learning models like YOLO is a complex process that often involves a lot of trial and error. That said, there are some guidelines you might find useful:

1. Learning rate (lr0): This is one of the most important hyperparameters. The optimal learning rate can depend on many factors, but a common initial learning rate for YOLO models is around 0.001. However, if your training is unstable or not improving, you might want to try lowering the learning rate. Some researchers also use learning rate schedules that start with a higher learning rate and then decrease it over time.

2. Final learning rate (lrf): This is the learning rate at the end of training. A common practice is to reduce the learning rate gradually during training to help the model converge. A typical value might be 0.0001 or even lower, but again, this can depend on many factors.

3. Batch size: Larger batch sizes can provide a more accurate estimate of the gradient, but they also require more memory. If you have enough memory, you might want to try increasing the batch size.

4. Number of epochs: More epochs can lead to better performance, up to a point. However, too many epochs can lead to overfitting, especially if you don't have a large dataset. A common practice is to use early stopping, which means ending training when performance on a validation set stops improving.

5. Data augmentation: Techniques like random cropping, rotations, and flips can help to create a more robust model. They can be especially useful when you're working with a small dataset.

6. Optimizer: Different optimizers can lead to different results. The Adam optimizer is often a good choice for YOLO models, but you could also experiment with others.

For a large dataset like yours, it could be beneficial to use a tool like Optuna or Hyperopt for hyperparameter optimization. These tools can automate the process of finding the best hyperparameters for your model.

Additionally, keep in mind that achieving high accuracy in a task like license plate recognition can be challenging, and might require a large and diverse dataset. Make sure your training, validation, and test sets are representative of the data the model will encounter in production.

[newegy]

Thank you for answering, I already tried all above but I didn't try optuna . I'll try it and see if ill get a better result. For plate characters what do you think is the best argument?

[newegy]

The last question, do you think i should use segmentation rather than classification?

[glenn-jocher]

@newegy considering your specific task of license plate character recognition, using segmentation rather than classification largely depends on your particular use case and constraints. Both methods could potentially work well, but they come with different advantages and challenges.

1. Classification: If you're detecting each character as a separate object, you'll be using classification. This can be simpler to train and implement, and could be sufficient if your characters are well-isolated and distinguishable. It can, however, sometimes be less accurate if the characters are compressed or overlapping, as it operates on a per bounding box basis.

2. Segmentation: On the other hand, segmentation refers to pixel-wise prediction, which could provide more precise localization especially when characters are closely placed or overlapping. This can potentially be more accurate, but it's usually more complex and could require more computational resources.

In summary, if you're having trouble with accuracy using classification (detection), it could be worth it to try segmentation approach, but be prepared for potentially more complex training and inference processes. As always, the right approach depends on your specific problem and constraints, so experimentation is key.

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