| --- |
| 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. |
| |
