Comparison of YOLOv8 model about ultralytics

[glenn-jocher]

@AlvinBimo23 hello! Great observation! 🕵️‍♂️ Indeed, larger models generally take longer to train due to their complexity. However, training time can also be influenced by several other factors:

• Data Efficiency: Some models, despite being larger, may converge faster on certain types of data due to their architecture specifics.
• Optimizer and Learning Rate Settings: Different settings can lead to variations in convergence speed.
• Hardware Utilization: Depending on how well a model utilizes the underlying hardware, training times can vary, even for larger models.

Assessing performance isn't only about the size but how each model optimizes the use of resources and data during training. If you have specific logs or settings, feel free to share them for a deeper analysis! 🚀