YOLOv8 classification about ultralytics

[github-actions]

👋 Hello @bezakovabarbora, 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

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

@bezakovabarbora hello! It seems like you're noticing differences in model performance based on the type of data used for validation. This typically happens when there's a significant difference between the distribution of your training data and validation data, especially evident when using AI-generated images versus real-world images.

AI-generated data might not comprehensively capture the variability and complexity found in real-world data. This discrepancy can lead the model to overfit to the characteristics of the generated images, which might not generalize well to real-world scenarios, thus performing poorly when real data is introduced.

To mitigate this, try to ensure the AI-generated data closely mirrors the real-world data in terms of distribution and characteristics. Also, regularly validate with a mix of both real and generated data can give you a better insight into how well the model might perform in actual applications. Here's a snippet to set up your validation process to include both types of data:

from ultralytics import YOLO

# Load the model
model = YOLO('yolov8n-cls.pt')

# Train the model using both generated and real-world images
results = model.train(data='combined_data.yaml', epochs=100, imgsz=640)

# Evaluate the model
metrics = model.val()

Make sure combined_data.yaml contains paths to both your AI-generated and real-world validation datasets. This approach can help achieve more robust generalization. Hope this helps! 🚀

[bezakovabarbora]

@glenn-jocher Thank you for your response. I understand that the photos are from different distribution, I will try to combine it. Does YOLO use the validation set in the training process to adjust the parameters during training?

[glenn-jocher]

Hello @bezakovabarbora! Great to hear that you'll try combining the datasets. Regarding your question, YOLO does not use the validation set to adjust the model's parameters during training. The validation set is solely used to evaluate the model's performance after each epoch, ensuring that the model generalizes well to new, unseen data without influencing the training process directly. This helps in monitoring for overfitting and tuning hyperparameters effectively. Let me know if you need further clarification! 😊