Label images as defective or not defective.
Reduce judgment differences between annotators.
Choose two data practices.
Document boundary and missing cases and measure agreement among annotators, reviewing disputed examples instead of deleting them blindly.
Detailed explanation
Annotators can apply a consistent rule.
Annotators can apply a consistent rule.
Label uncertainty becomes visible.
Label uncertainty becomes visible.
Consistency and reproducibility are lost.
Consistency and reproducibility are lost.
Difficult cases and production behavior may disappear.
Difficult cases and production behavior may disappear.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 1.2のデータラベリング、品質、機械学習ライフサイクルを確認する。Expected result
ラベルの一貫性と曖昧さを測定・改善する手順を説明できる。Key points
- Label guideline
- Boundary case
- Agreement
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.