Reporting Bad Cases¶

VMAF's predictions do not always reflect perceived quality — corner cases and novel application scenarios outside the training distribution both produce mispredictions. Bad-case reports are valuable for improving future model versions.

Upstream channel (Netflix/vmaf)¶

Netflix maintains a Google form to collect bad-case samples. Users can opt in or out for public sharing:

• Bad-case submission form

Fork channel (VMAFx/vmafx)¶

For bad cases that are specific to fork-added surfaces — SYCL / CUDA / HIP numerical divergence, --precision output correctness, tiny-AI model drift — open an issue on VMAFx/vmafx with reproducer inputs and, if possible, the backend that produced the anomalous result.

A cross-backend numeric diff can be generated via the /cross-backend-diff skill before filing, which narrows the report to the specific feature and scale where the divergence is observed.