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.

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 §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, 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. See eval_3/tracks/TRACK_OBJECTVLA.md in the source repo for the full training recipe.

Loading example

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}")