Hyperparameter Optimization Results for SAM3Text on Synthetic and Real IRM Data

A 9.5 KB Excel file containing results from a hyperparameter optimization study. The work by Mario Koddenbrock, last updated in May 2026, demonstrates that tuning on synthetic SynthMT images can improve the SAM3Text model to human-grade performance on unseen, real IRM data.

Use Cases

• Benchmarking hyperparameter optimization techniques based on the described synthetic-to-real performance transfer.
• Analyzing the relationship between synthetic data tuning and real-world model performance as indicated in the description.
• Reproducing or extending the study on improving model robustness for unseen medical imaging data.

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• File size is small (9.5 KB), ensuring quick download and inspection.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Mario Koddenbrock via figshare

Collection Method

Likely contains results from computational experiments in hyperparameter optimization.

Freshness

Last updated 2026-05-05 17:41:24; freshness should be verified.