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README.md

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+# eval3_objectvla_vl_pairs
+
+ObjectVLA-style vision-language co-training data for the Eval 3 face/celebrity
+identification task. Companion dataset to
+[`HBOrtiz/so101_eval3_aug_v3_200celebs`](https://huggingface.co/datasets/HBOrtiz/so101_eval3_aug_v3_200celebs).
+
+## Contents
+
+- **`manifest.parquet`** — 176 670 VL pairs (one row per (frame, portrait, caption_type)).
+- **`data.tar.zst`** — 1.7 GB compressed archive containing:
+  - `images/chunk-{000..003}/*.jpg` — 29 445 wrist-cam frames (480 × 640, 3 sampled per episode at 20% / 50% / 80%).
+  - `references/<episode>__ref.jpg` — 9 815 reference photos (480 × 480, one per episode — the "what the target should look like" cue).
+  Extract with `tar --zstd -xf data.tar.zst` (creates the two subdirs).
+- **`_stats.json`** — dataset statistics + skip counters.
+
+## Manifest schema
+
+| column | type | meaning |
+|---|---|---|
+| `image_path` | str | path to wrist-cam JPEG (e.g. `images/chunk-001/quick_lecun_LSO_ep01_..._var00__f0107.jpg`) |
+| `reference_image_path` | str | path to reference JPEG (e.g. `references/quick_lecun_LSO_ep01_..._var00__ref.jpg`) |
+| `prompt` | str | the input prompt |
+| `target` | str | the expected completion |
+| `bbox_xyxy_norm` | list[float] | bounding box as `[x1, y1, x2, y2]` normalized to [0, 1] in (W, H) of the wrist-cam image |
+| `celeb_name` | str | celebrity display name (e.g. `"Yann LeCun"`) |
+| `celeb_slug` | str | slug form (e.g. `"yann_lecun"`) |
+| `caption_type` | str | `"location_explicit"` or `"qa_grounded"` |
+| `episode` | str | source episode in the robot dataset |
+| `frame_idx` | int | frame index in the source mp4 |
+| `pid` | int | portrait id (0, 1, or 2) |
+
+## Two camera streams per sample
+
+Mirroring the original Eval 3 robot dataset's two-camera setup:
+
+- **`image_path`** is the **wrist-cam** view — 480 × 640 of the workspace with 3 visible printed portraits. This is the perception domain the robot acts in.
+- **`reference_image_path`** is the **reference** view — 480 × 480 of the target celeb's photo, the same image for all frames of a given episode (it's a constant-frame video in the source robot dataset).
+
+The bbox in `bbox_xyxy_norm` is the **paper-region bbox in the wrist-cam image** (where the target portrait sits on the table), NOT the reference image (which is whole-frame). Use the reference image as auxiliary visual context, not for bbox grounding.
+
+## Two caption types
+
+Per visible portrait per frame we emit one row of each type (so 6 rows per
+frame for 3 visible portraits):
+
+**`location_explicit`** — model is asked what's in the image, expected to emit name + bbox:
+```
+prompt: "What is in this image?"
+target: "The printed photo of Barack Obama is at [0.06, 0.43, 0.41, 0.90]."
+```
+
+**`qa_grounded`** — model is given a bbox in the prompt, expected to name the person there:
+```
+prompt: "Who is in the printed photo at [0.06, 0.43, 0.41, 0.90]?"
+target: "Barack Obama"
+```
+
+## Why this format
+
+Following the ObjectVLA recipe ([arxiv 2502.19250](https://arxiv.org/abs/2502.19250) §3),
+the bbox is **whole-object** (the printed portrait rectangle), computed
+from the augmentation pipeline's cached `portrait_corners.json` (no new
+face-detection inference required). Loose-crop / whole-object boxes
+empirically outperform tight face crops for identity discrimination
+([Banerjee 2022](https://arxiv.org/abs/2208.02991), LFW ablations).
+
+## Stats
+
+- 192 unique celebrities
+- 9 815 source episodes (of 9 842 in the robot dataset — 27 with missing corners metadata)
+- 29 445 sampled wrist-cam frames (3 per episode, at 20% / 50% / 80% time fractions)
+- 9 815 reference frames (1 per episode — constant-frame video, single sample taken)
+- 176 670 VL pairs (50 / 50 split between `location_explicit` and `qa_grounded`)
+
+## Intended use
+
+Mix into Pi0.5 fine-tuning at a 10:1 robot:VL batch ratio per
+[ObjectVLA](https://arxiv.org/abs/2502.19250). See
+[`eval_3/tracks/TRACK_OBJECTVLA.md`](https://github.com/Ace3Z/LeMonkey/blob/main/eval_3/tracks/TRACK_OBJECTVLA.md)
+in the source repo for the full training recipe.
+
+## Loading example
+
+```python
+import subprocess
+from huggingface_hub import hf_hub_download
+import pyarrow.parquet as pq
+
+# Download + extract the image archive
+tar = hf_hub_download("HBOrtiz/eval3_objectvla_vl_pairs",
+                        "data.tar.zst", repo_type="dataset")
+subprocess.run(["tar", "--zstd", "-xf", tar])  # creates images/ and references/
+
+# Load manifest
+manifest = pq.read_table(hf_hub_download(
+    "HBOrtiz/eval3_objectvla_vl_pairs", "manifest.parquet", repo_type="dataset"
+)).to_pandas()
+
+# Sample one row
+row = manifest.iloc[0]
+print(row.prompt, "→", row.target)
+print(f"wrist-cam: {row.image_path}")
+print(f"reference: {row.reference_image_path}")
+```

_stats.json

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+{
+  "n_records": 176670,
+  "n_episodes_processed": 9815,
+  "skipped_no_video": 0,
+  "skipped_no_corners": 0,
+  "skipped_no_pid_map": 0,
+  "skipped_frame_decode": 0,
+  "skipped_no_corners_for_pid": 0,
+  "n_unique_celebs": 192,
+  "n_unique_images": 29445,
+  "wall_seconds": 68.9789981842041
+}

data.tar.zst

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+size 1723187307

manifest.parquet

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