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

[newegy]

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?

[hannaliavoshka]

From the description, I can assume you face a bias problem (underfitting) when the model performance on the training set is not satisfying.
I would suggest trying the following:

• train longer (for more epochs),
• increase training image size (default is 640), if applicable for your dataset,
• try larger model architecture than the current (m, l, x).
  What about the recommended values for parameters, I suppose they are already used as default (https://docs.ultralytics.com/modes/train/#arguments).
  Still, you need to experiment with them if the model performance is not satisfying.

[newegy]

i tried training for 15,000 epochs but from the 2000 epochs i am getting no new best , imgsz is 1024 , do you know why i am getting no best after 2000 epochs

[glenn-jocher]

@newegy it sounds like your model may have reached a plateau in learning, which is why you're not seeing improvements after 2,000 epochs. Here are a few suggestions:

1. Learning Rate Schedule: Adjust the learning rate schedule. Sometimes, a lower learning rate towards the end of training can help the model fine-tune its weights. However, be cautious of reducing it too much as it may cause the training to stagnate.

2. Early Stopping: Implement early stopping to prevent overfitting and save time. If the model isn't improving after a certain number of epochs, it's often better to stop the training.

3. Data Augmentation: Increase data diversity through more aggressive data augmentation. This can help the model generalize better.

4. Regularization: If you suspect overfitting, increase regularization methods like weight decay.

5. Model Complexity: Since you're using YOLOv8m, consider trying YOLOv8l or YOLOv8x for potentially better feature extraction capabilities.

6. Hyperparameter Tuning: Experiment with other hyperparameters. Sometimes, slight adjustments can lead to better performance.

Remember, training for more epochs doesn't always guarantee better performance, especially if the model has already converged. It's about finding the right balance and combination of parameters and techniques.

[hannaliavoshka]

2,000 epochs sound pretty much to see the difference between model performance trained on the 2,000 and 25,000 sample datasets. If the difference is irrelevant, it can be the signal for switching to a more complex model architecture (with more hidden layers, for instance, try yolov8l.pt instead of yolov8m.pt and so on). The model performance cannot improve endlessly with epochs number increasing: sooner or later the model will learn all the relevant information from the dataset on the level it can considering its architecture ability and training hyperparameters.
If a larger model will not show performance improvement, it will be time to explore the dataset quality and diversity for each of your 45 classes. To analyze what mistakes are common for the model, what classes are the most problematic, and why.
I cannot suggest exact hyperparameters values, because every problem is different, and you need to experiment to find the best solution. But from my experience, it is very important to get a sufficient quantity of quality data for every class, an enough complex model to generalize all relevant information from this data, and a thoughtful solution strategy for your problem generally.

Here are brief Tips for Best Training Results for yolov5, I believe they are useful for v8 too. Hope it will help to analyze your problem!
https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results

[glenn-jocher]

@hannaliavoshka to optimize training parameters for your YOLOv8m model with a large license plate character dataset, you should consider experimenting with the following:

1. Learning Rate: Adjust the initial learning rate (lr0) and final learning rate (lrf) to find a balance that allows the model to converge effectively. A lower learning rate can lead to better convergence but may require more epochs.

2. Batch Size: A larger batch size can provide more stable gradient estimates, but be mindful of your hardware limitations.

3. Epochs: While more epochs can lead to better performance, there is a point of diminishing returns. Monitor your validation metrics to decide when to stop training.

4. Data Augmentation: Experiment with different data augmentation techniques to improve the model's generalization.

5. Class Balance: Ensure that your dataset has a balanced representation of all classes to prevent bias towards more frequent classes.

6. Model Complexity: If the YOLOv8m model plateaus in performance, consider trying a larger model variant like YOLOv8l or YOLOv8x for potentially better feature extraction capabilities.

7. Regularization: Techniques like dropout or weight decay can help prevent overfitting, especially when training on large datasets.

Remember, each dataset is unique, and finding the optimal set of hyperparameters often requires empirical testing. Utilize the Ultralytics Docs for guidance on using the different modes effectively, and consider using the hyperparameter evolution feature to automate the search for optimal values. Good luck with your training! 🚀