P-057-RisingStar/scenesearch-assets

SceneSearch corpus assets

The two things SceneSearch needs at runtime that are too large to keep in git: the search index and the frame thumbnails, both covering 6,858 CAM_FRONT keyframes from 170 nuScenes v1.0-trainval scenes.

This is derived data, not a source. The authoritative record of a frame is its coordinate — video_id plus timestamp_s in the application's frames.jsonl, which is committed — and everything here is regenerable from nuScenes by re-running the project's ingest. Nothing is derivable only from this repo.

Contents

vectors.npy, caption_vectors.npy   ->  artifacts/
thumbs.tar                         ->  repo root      <- fetch this one
<frame_id>_CAM_FRONT.jpg           ->  repo root, flat

artifacts/vectors.npy	21 MB	6858 × 768 float32, L2-normalised SigLIP2 image embeddings
artifacts/caption_vectors.npy	10.5 MB	6858 × 384 float32, MiniLM embeddings of generated captions
thumbs.tar	82 MB	all 6,858 thumbnails, flat and uncompressed
*.jpg	70 MB	the same thumbnails loose, 320×180, downscaled from 1600×900

Fetch thumbs.tar, not the loose files. They hold identical bytes, and the loose copies exist so the dataset is browsable here — but pulling 6,858 files one at a time is what the Hub rate-limits an unauthenticated caller for. It returned 429 at roughly the 490th file on a CI runner, four and a half minutes in, leaving a half-populated directory. One request cannot half-arrive.

The tarball is uncompressed deliberately: measured over 200 of these thumbnails, gzip -6 saves 2.1%, because JPEG is already DCT-compressed. It is larger than the sum of its contents (82 MB against 70) because tar pads every member to a 512-byte boundary and these files are small — that padding is the price of the single request, and it compresses away in transit.

Row order in both matrices matches frames.jsonl in the application repo exactly, and the application re-checks that on load — a misaligned row would mean confident answers about the wrong frames. Thumbnails are named <frame_id>_CAM_FRONT.jpg, where frame_id is the nuScenes sample_data token.

The thumbnails sit at the repo root rather than under a matching thumbs/ prefix only because moving 6,858 published files server-side would cost a full re-upload for cosmetics.

The 170 scenes were not chosen at random. They were selected so that rain and night appear independently rather than together — in the 10-scene subset this project started from, every rainy scene was also a night scene, which makes the two conditions impossible to tell apart when measuring. See the project's eval/results/report.md for the resulting measurements.

Usage

From a SceneSearch checkout, both are fetched by one script — and make run and make test call it already, so a fresh clone needs no extra step:

python scripts/fetch_assets.py              # index -> data/artifacts/, thumbnails -> data/thumbs/
python scripts/fetch_assets.py --what index # just what the service cannot start without

Directly:

import tarfile
from huggingface_hub import hf_hub_download

REPO = "P-057-RisingStar/scenesearch-assets"
hf_hub_download(REPO, "artifacts/vectors.npy", repo_type="dataset", local_dir="data/artifacts")

tar = hf_hub_download(REPO, "thumbs.tar", repo_type="dataset", local_dir="data")
with tarfile.open(tar, "r:") as t:
    t.extractall("data/thumbs", filter="data")   # filter= requires Python 3.11.4+

SceneSearch pulls both at image build time, never per request, so the running service reads from its own disk and does not depend on this repo being reachable. The index pull is fatal and the thumbnail pull is not: without the index the service cannot answer at all, while without thumbnails it answers correctly and shows broken images.

Licence and attribution

Derived from nuScenes by Motional, released under CC BY-NC-SA 4.0. These assets are a derivative work and carry the same licence: attribution, non-commercial, share-alike.

nuScenes: A multimodal dataset for autonomous driving. Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom. CVPR 2020.

Non-commercial use only, per the upstream licence. If you need these images commercially, go to nuScenes directly — this repo cannot grant terms it does not hold.

Provenance

Produced by scripts/publish_assets.py in the SceneSearch repository. Regenerating them from a nuScenes install and re-running that script reproduces this repo exactly.