Comments (2)
Hello! The discrepancy you're seeing between the outputs of model.info
and the val
command is likely due to different levels of detail each function is designed to provide. The model.info
generally provides a detailed summary including all layers and parameters explicitly defined in your model. On the other hand, the val
command focuses more on performance metrics and may summarize the model architecture more broadly.
To get a detailed parameter printout similar to what you see in a .yaml
file post-training, you can use the model.yaml()
method to print or even save your model definition:
print(model.yaml()) # to display on screen
model.yaml(save_path='model_details.yaml') # to save to a file
This will generate output similar to the initial YAML configuration, reflecting the full list of layers and parameters used in your model. If youβre specifically looking to match the visualization format in the last image, consider custom scripts or tools that format layer and parameter data accordingly. Happy modeling! π
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π 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.
For additional resources and information, please see the links below:
- Docs: https://docs.ultralytics.com
- HUB: https://hub.ultralytics.com
- Community: https://community.ultralytics.com
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 π and Vision AI β
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