license: mit
tags:
- facial-expression
- action-units
- emotion-recognition
- gaze-estimation
- benchmark
pretty_name: py-feat benchmarks
configs:
- config_name: accuracy
data_files: accuracy.csv
- config_name: throughput
data_files: throughput.csv
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 detector frames/sec across hardware × batch. |
cross_tool_methodology.md |
How every tool was run end-to-end, the feature matrix, dataset/protocol choices, and per-tool integration notes. |
competitors/*.json |
Raw 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.