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README.md

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+---
+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](https://github.com/cosanlab/py-feat) and a
+cross-tool comparison against **OpenFace 3.0**, **LibreFace**, and **PyAFAR**.
+Powers the [py-feat live dashboard](https://py-feat.org). 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.