Dataset card: Stage-3 fine-tuning uses all three styles

README.md

@@ -55,7 +55,7 @@ PersonaDrive retrieves frames of the selected style as in-context demonstrations
 | `slow` | Conservative | 13,021 |
 | **Total** | | **29,777** |
 
-These are the frames the 15 participants drove (`driver1`–`driver15`). The `fast` and `slow` folders also contain `lb1_split`: routes of SimLingo's Leaderboard 1.0 split, recorded in the aggressive and conservative style and used for Stage-3 fine-tuning. They are not part of the participant data and are not counted above.
+These are the frames the 15 participants drove (`driver1`–`driver15`). The `fast` and `slow` folders also contain `lb1_split`: routes of SimLingo's Leaderboard 1.0 split, recorded in the aggressive and conservative style. They are not part of the participant data and are not counted above. Stage-3 fine-tuning uses data from all three styles.
 
 Each frame has a front camera image, the ego state and controls, the navigation targets and the surrounding objects. Most routes also have per-frame commentary and VQA annotations.
 
@@ -90,7 +90,7 @@ for z in *.zip; do unzip -q "$z" -d "$PERSONADRIVE_DATA_ROOT"; done
 |---|---|
 | Use the driving data of one style | `data_<style>` (plus `commentary` / `drivelm` for language) |
 | Evaluate PersonaDrive with a style (`default` / `fast` / `slow`) | `data_<style>` of that style |
-| Re-run Stage-3 fine-tuning | `data_fast`, `data_slow`, `commentary`, `drivelm`, plus SimLingo's PDM-lite data |
+| Re-run Stage-3 fine-tuning | `data_default`, `data_fast`, `data_slow`, `commentary`, `drivelm`, plus SimLingo's PDM-lite data |
 | Re-run Stages 1–2 (retrieval head, per-style indices) | all five zips, plus PDM-lite for the style-agnostic `normal` index |
 
 Evaluation with retrieval opens the camera frames of the retrieved demonstrations, so it needs the frames of the chosen style. It also needs the retrieval head, which is in the [code repository](https://github.com/PervasiveAutonomyLab/PersonaDrive/blob/main/persona_drive_rag/checkpoints/best_model.pt), and the per-style FAISS indices, which you build from this dataset ([Build the retrieval indices](https://github.com/PervasiveAutonomyLab/PersonaDrive#3-build-the-retrieval-indices)). The code repository's [`datasets/README.md`](https://github.com/PervasiveAutonomyLab/PersonaDrive/blob/main/datasets/README.md) lists which zips each use needs.