Significant Drop in Performance when Switching between YOLOv8n-seg Models about ultralytics HOT 4 OPEN

👋 Hello @AirdEliya, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

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• Docker Image. See Docker Quickstart Guide

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@AirdEliya hello,

Thanks for bringing this issue to our attention and for the detailed description of your experience with the YOLOv8n-seg models. It sounds like you've been very thorough in your approach. 🕵️‍♂️

Switching models can sometimes lead to performance variations due to differences in model architectures and how they handle feature learning, particularly when moving to a variant like yolov8n-seg-p6. Adjustments in hyperparameters, as you've attempted, are a good response, but the issue might also relate to other factors such as initialization states or minor changes in training conditions that might not be immediately apparent.

A potential first step to diagnose the issue could be to ensure consistency in the model's state before and after switching models or performing significant training operations like using the copy_paste function. You could also try monitoring the intermediate outputs and training dynamics to spot any abrupt changes or anomalies.

Here’s a simple code snippet to check for model parameter consistency:

def check_model_consistency(model1, model2):
    for p1, p2 in zip(model1.parameters(), model2.parameters()):
        if not torch.allclose(p1, p2):
            return False
    return True

# Usage:
is_consistent = check_model_consistency(trainer.model, previous_model_state)
print('Model consistency:', is_consistent)

This is simplistic and assumes you have access to both model states but could be a starting point to ensure that the models’ parameters aren’t altered unintentionally.

We're here to help and would appreciate your continued feedback or contributions, especially if you discover a solution that might benefit others encountering similar challenges!

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Hello author @glenn-jocher ,

I wanted to inform you that I have resolved the issue I previously inquired about. It turns out the solution was quite simple. When using the yolov8n-seg model, I mistakenly called the command as instructed in the Segmentation task using yolov8n-seg.pt. Consequently, the .pt file was downloaded to the current path. I haven't had the chance to delve into this file to understand its origin as a pre-trained model.

Additionally, I initially intended to call yolov8n-seg-p6.yaml to utilize the initial architecture. However, when reverting back, I accidentally used yolov8n-seg.yaml for training, resulting in differences between the two runs. I apologize for any inconvenience caused by these inquiries.

Moving forward, I have a few more questions. Once a good epoch is found using the pre-trained models with the initial conditions and achieving a good mAP with the validation data, how should other parameters be adjusted? Moreover, what strategy should be employed for data augmentation to gradually adjust the parameters to fit my dataset?

I would like to know how the pretrained yolov8n-seg be trained.

Thank you.

from ultralytics.

Hello @AirdEliya,

Great to hear that you've resolved the issue! It's easy to mix up file names, especially when switching between similar model configurations. 😅

For your questions on fine-tuning the model:

1. Adjusting Parameters: After finding a good epoch, consider adjusting the learning rate and batch size to see if you can squeeze out more performance without overfitting. Also, experiment with different optimizers if you haven't settled on one yet.
2. Data Augmentation: Start with basic augmentations like rotation, flipping, and scaling. Gradually introduce more complex augmentations like color adjustments and noise. Monitor the model's performance on validation data with each change to ensure improvements.

To train the pretrained yolov8n-seg model, you can use the following example command:

yolo detect train model=yolov8n-seg.pt data=your_dataset.yaml

Make sure your dataset is properly configured in the .yaml file.

Keep experimenting, and don't hesitate to reach out if you have more questions! 🚀

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