ASR-Bench-1k

Browse all 1,000 questions with visual previews

Select the preview subset in the Dataset Viewer for image previews and 16-frame video contact sheets. Single-scene questions have one input; cross-scene questions show A and B separately. Questions and sample IDs are unchanged. Previews omit answers.

The original public subset remains the default to preserve existing programmatic loading behavior. Preview images are resized browsing aids, not evaluation media. Video sheets show F1-F16 in row-major order using the exact released sampling indices; they are not video players. Original videos remain in the download archives.

ASR-Bench-1k is a 1,000-question spatial measurement evaluation set drawn from 50 official ScanNet++ validation scenes. It contains images and videos, single-scene and cross-scene questions, and given-reference and self-selected-reference reasoning. This release is for evaluation, not training.

Dimension	Count
Image / video questions	751 / 249
Single-scene / cross-scene questions	427 / 573
Given / self-selected reference	782 / 218
Distance / size questions	744 / 256
Scenes / unique media files	50 / 902

Visual examples

These are browsing previews only. Open all 1,000 samples.

Example 1: image, single_scene

[image] The vertical extent of the bookshelf in the image is given as approximately 2.6 meters. Using this image, approximately how far apart are the centers of the whiteboard in the image and the keyboard in the image in 3D?

Sample ID: 001e6903e380f50c6bc10549c1b68306.

Example 2: video, cross_scene

Context video A: [video] Context video B: [video] The vertical extent of the leftmost kitchen cabinet in video A is given as approximately 0.75 meters. The door in video A and the door in video B are explicitly linked as having the same vertical extent. Using both videos, approximately what is the vertical component of the closest 3D distance between the twelfth-from-left chair in video B and the leftmost ceiling lamp in video B?

Sample ID: 00509805f1e11ad8ce67ba91b14a3fb3.

Repository layout

README.md
SOURCE_README.md                 # Original local documentation, historical context
manifest.json                    # Original release manifest, retained unchanged
upload_manifest.json             # Artifact and source-file sizes and SHA-256 hashes
release/
  benchmark_public_v3.jsonl       # 1,000 model-facing inputs
  benchmark_hidden_v3.jsonl       # 1,000 evaluator-only annotations
preview/
  test.parquet                   # Optional visual browsing subset
  public.parquet                 # Lossless public-input mirror for HF loading
  manifest.json                  # Preview audit and hashes
  build_preview.py               # Reproducible preview builder
archives/
  images.tar.gz                  # Extracts images/...
  videos.tar.gz                  # Extracts videos/...
  sidecar_v3.tar.gz               # Extracts sidecar_v3/...
  reference_provenance.tar.gz     # Extracts provenance/ and release/coordinate_train_v3.jsonl

The original manifest.json and SOURCE_README.md describe the local release before this upload. Their local filesystem paths and historical upload status are retained for provenance; use upload_manifest.json and this README for the HF layout.

Download, verify, and extract

Install huggingface_hub, then download the repository. All archives must be extracted into the same root containing release/. No paths or indices need rewriting. Use Python 3.12+ for the extraction example.

from huggingface_hub import snapshot_download
from pathlib import Path
import hashlib
import json
import tarfile

root = Path(snapshot_download(
    repo_id="AnchorSR/ASR-Bench-1k",
    repo_type="dataset",
    local_dir="ASR-Bench-1k",
    ignore_patterns=["preview/*"],  # Optional visual browser assets are not needed for evaluation.
    # Set revision="<commit SHA>" to pin an exact release.
))
manifest = json.loads((root / "upload_manifest.json").read_text())
for relative, expected in manifest["artifacts"].items():
    path = root / relative
    digest = hashlib.sha256()
    with path.open("rb") as handle:
        for block in iter(lambda: handle.read(8 * 1024 * 1024), b""):
            digest.update(block)
    assert path.stat().st_size == expected["bytes"]
    assert digest.hexdigest() == expected["sha256"]
for archive in sorted((root / "archives").glob("*.tar.gz")):
    with tarfile.open(archive) as handle:
        handle.extractall(root, filter="data")

For numerical evaluation only, the public/hidden JSONL and image/video archives are sufficient. Sidecar and reference/provenance archives support grounding and audit work. The default HF dataset configuration exposes only the original public test inputs; media paths are relative strings and require the archive extraction above.

Model inputs and evaluator annotations

Public records retain their original sample_id, media, images, videos, messages, and version fields. Keep media in placeholder order and preserve input roles (scene, or scene_a / scene_b). Original system messages request the v3 natural-language plus symbolic reasoning format. Baseline-specific prompt adaptations are evaluation-side decisions and must be documented separately.

Every video uses uniform16-linspace-floor-v1: for a decoded video of N frames, select (i * (N - 1)) // 15 for i=0..15, independently per video. Public media metadata records the exact local clip frame indices. Symbolic frame IDs 1..16 refer to those selected model-visible frames, not to original source-video frame numbers.

The hidden JSONL includes answer (published estimate), answer_interval_m (accepted interval), gt_value (geometric numerical target), reference reasoning, object bindings, and Sidecar annotations. Metric values are in metres. Hidden means excluded from model input, not access-controlled: these annotations are downloadable from this public repository. Never feed them, the complete reference records, or provenance gold information to the evaluated model.

release/coordinate_train_v3.jsonl retains its historical filename for compatibility; it is a reference-only representation of the same 1,000 evaluation samples and must not be used as training data. Sidecar scene/frame geometry retains a documented parent support superset; it does not add benchmark questions.

Validation and limitations

The published subset contains 1,000 unique aligned IDs across public, hidden, reference, and sample-level Sidecar tables. Packaging preserves original record and media bytes, and every unpacked source file was verified by SHA-256. The package also checks 902 media references and published uniform16 index arithmetic. Packaging does not claim a new full-media decode or an independent geometric remeasurement.

This is a reviewed subset of the original 2,000-question candidate benchmark. Selection used answer-visible model visual QA, not independent human annotation. The selected samples were assessed as usable or usable with minor notes; this does not guarantee numerical ground-truth accuracy. Parent 2k acceptance/review reports in provenance remain historical reports, not newly run 1k evaluations.

Media originates from ScanNet++; applicable upstream data terms continue to apply. This package does not grant additional rights over the source media.

Optional visual preview

preview/test.parquet embeds JPEG thumbnails in two Image-typed columns, input_a and input_b, alongside the original question and sample ID. input_b displays a NO SECOND INPUT placeholder for single-scene samples. This is a browser-only card, not an additional media input. preview/manifest.json records source hashes, preview hashes, and exact video sampling indices. preview/build_preview.py reproduces the browser assets from the extracted release. These optional files are independent of the release JSONL, archive hashes, and evaluation paths. An unfiltered whole-repository download also fetches previews; use the ignore_patterns example above to avoid that extra data.

from datasets import load_dataset

# Original model-facing input schema (also the default).
public = load_dataset("AnchorSR/ASR-Bench-1k", "public", split="test")
# Optional visual browsing representation; not a model evaluation input source.
preview = load_dataset("AnchorSR/ASR-Bench-1k", "preview", split="test")

preview/public.parquet is a lossless mirror of the original public JSONL used solely to keep both HF configurations on the same Parquet loader. Its records and feature schema are checked against the original JSONL; the original release JSONL remains unchanged and is still the canonical downloadable evaluation artifact.

The optional browser files add approximately 162 MiB to an unfiltered snapshot. The root upload_manifest.json lists required release artifacts under artifacts and optional browser artifacts separately under optional_artifacts.

For single-scene questions, the B column displays “NO SECOND INPUT” instead of an empty Image cell, avoiding the HF Viewer’s misleading pagination warning. Actual input counts and paths remain unchanged in public and the release JSONL.