Rodrigo/modeltype parameter #401
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iurisilvio
reviewed
Aug 8, 2025
tests/manual/debugme.py
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| def run_api_train(): | ||
| rf = Roboflow() | ||
| project = rf.workspace("model-evaluation-workspace").project("penguin-finder") | ||
| # version_number = project.generate_version( | ||
| # settings={ | ||
| # "augmentation": { | ||
| # "bbblur": {"pixels": 1.5}, | ||
| # "image": {"versions": 2}, | ||
| # }, | ||
| # "preprocessing": { | ||
| # "auto-orient": True, | ||
| # }, | ||
| # } | ||
| # ) | ||
| version_number = "18" | ||
| print(version_number) | ||
| version = project.version(version_number) | ||
| model = version.train( | ||
| speed="fast", # Options: "fast" (default) or "accurate" (paid feature) | ||
| checkpoint=None, # Use a specific checkpoint to continue training | ||
| modelType="rfdetr-nano", | ||
| ) | ||
| print(model) | ||
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| if __name__ == "__main__": | ||
| # run_cli() |
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I think all these changes were for debug?
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I understood this file is more of a custom test file, with no real utility at the end project. But I will remove this, it keeps less diffs.
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- Add model_type parameter to CLI train command - Update version.py to handle model_type in train method - Snake_case naming convention for model_type parameter - Simplify CLI workspace handling - Maintain API payload order minimal
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iurisilvio
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Aug 11, 2025
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Description
Creates modelType parameter to train method.
Type of change
Please delete options that are not relevant.
How has this change been tested, please provide a testcase or example of how you tested the change?
staging
Creating models with different types of model. And also sending invalid modelTypes.
Any specific deployment considerations
Docs