Cv differentiable soft f1 loss
Use this skill for the outcome in its description. Start with the supplied request and evidence, identify the specific task, and distinguish planning from execution. Do not trigger solely because the subject happens to be fashion, retail, or a technology named in an example. Read `references/execution-contract.md` before applying a retained source method.
Select the narrowest matching method below, then read its full entrypoint and the resources it requires. If multiple methods cover the same outcome, choose the one matching the user's environment and inputs; do not run them all. Retain each method's input requirements, decision steps, validation, output structure, and failure handling. If prerequisites are absent, use its honest proposal-only fallback or state that its documented path is blocked.
- **wenmin-wu/cv-differentiable-soft-f1-loss**: Use a soft macro-F1 loss `1 − mean(2·tp / (2·tp + fp + fn))` computed from raw sigmoid probabilities (no thresholding) as a direct training objective for multi-label classification, optionally combined with BCE — closes the gap between training surrogate and the F1 metric the leaderboard scores Read `references/wenmin-wu/skills/cv/differentiable-soft-f1-loss/SKILL.md`.
Return the requested deliverable with evidence, assumptions, validation performed, unresolved dependencies, and actual execution status. Source-specific results must be verified independently; importing these methods does not establish a working service connection.