Why is yolov5n better than yolov8n in my own dataset (e.g., P, R, MAP50, etc.)，even in coco128 about ultralytics HOT 3 CLOSED

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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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from ultralytics.

Hey there! 👋 It's quite intriguing that YOLOv5n is outperforming YOLOv8n on your dataset. There could be a few reasons for this, such as the architectural differences and how each model handles the specifics of your data. YOLOv8n has a much broader feature set and is typically more robust, but YOLOv5n might be better fitting for certain specific datasets due to varying hyperparameters or simpler architecture leading to less overfitting depending on the training scenario.

I recommend experimenting with different hyperparameter settings in YOLOv8, such as adjusting the learning rate, augmentation strategies, or even model configurations. Sometimes, a slight tweak can lead to significant improvements! Here’s a quick example on how you might adjust the training configuration using Python:

from ultralytics import YOLO

# Load model
model = YOLO('yolov8n.yaml')

# Train with custom hyperparameters
results = model.train(data='your_dataset.yaml', epochs=100, imgsz=640, lr0=0.01)

Make sure your dataset is well-represented in terms of variety and distribution. Keep us posted on your findings or if you have more questions! 🚀

from ultralytics.

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

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• Docs: https://docs.ultralytics.com
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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐

from ultralytics.