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py-feat/benchmarks

py-feat benchmarks Live benchmark data for py-feat and a cross-tool comparison against OpenFace 3.0, LibreFace, and PyAFAR. Powers the py-feat live dashboard. Updated by scheduled benchmark runs. Files File What accuracy.csv Tidy long table: one row per (tool, dataset, modality, metric). Covers AU F1 (DISFA+), 7-class emotion (AffectNet-val, RAF-DB), valence/arousal CCC (AffectNet-val), and gaze angular error (Columbia). throughput.csv py-feat… See the full description on the dataset page: https://huggingface.co/datasets/py-feat/benchmarks.

sourceHugging Facemitupdated 3mo agoView on Hugging Face
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py-feat benchmarks

Live benchmark data for py-feat and a cross-tool comparison against OpenFace 3.0, LibreFace, and PyAFAR. Powers the py-feat live dashboard. Updated by scheduled benchmark runs.

Files

FileWhat
accuracy.csvTidy long table: one row per (tool, dataset, modality, metric). Covers AU F1 (DISFA+), 7-class emotion (AffectNet-val, RAF-DB), valence/arousal CCC (AffectNet-val), and gaze angular error (Columbia).
throughput.csvpy-feat detector frames/sec across hardware × batch.
cross_tool_methodology.mdHow every tool was run end-to-end, the feature matrix, dataset/protocol choices, and per-tool integration notes.
competitors/*.jsonRaw per-tool result JSONs (provenance for every number in accuracy.csv).

accuracy.csv columns

tool · dataset · modality (au / emotion / valence_arousal / gaze) · metric · value · n (samples scored) · notes.

Protocol in one paragraph

Every competitor runs end-to-end as shipped (its own face detector, its own models) on identical images/labels frozen into shared manifests — never as a py-feat dependency, each in its own isolated env. Held-out sets only (DISFA+, not DISFA, since DISFA trains several competitors). Emotion is scored on the 7 shared classes; gaze I/O conventions are resolved identically and in every tool's favor. See cross_tool_methodology.md for the full story, including what it took to get each competitor to run.