README.md

metadata

pretty_name: OneSearch-Video-Bench
language:
  - en
task_categories:
  - visual-question-answering
tags:
  - benchmark
  - visual-search
  - video
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: test
        path: test.jsonl

OneSearch-Video

OneSearch-Video contains 307 visual question-answer pairs over 293 silent videos. The distributed MP4 files play at 1 FPS. Each playback second represents one ordered frame slot in the benchmark input. This is the playback rate of the sampled sequence, not a claim that the source footage was sampled once per second.

Files

README.md
prepare.py
extract_frames.py
test.jsonl
media.jsonl
frames.jsonl
alignment.json
media/
  0000.mp4
  ...
runtime/frames/
  0000/frame_000.png
  ...

Videos are distributed in video-00001.zip. Original-resolution tool frames are distributed separately in video-frames-*.zip. Extract both the video archive and every frame archive into this directory, preserving their paths. Each archive is at most 10 GB (10,000,000,000 bytes). The PNGs preserve the annotation images exactly, including their original resolution and file bytes. They are not decoded from the smaller MP4s.

From the distribution directory:

unzip video-00001.zip
for archive in video-frames-*.zip; do unzip "$archive"; done

test.jsonl has one record per question:

Field	Meaning
id	Stable sample ID, represented as a string
question	Question given to the model
answer	Reference answer for evaluation; not an actor input
video	MP4 path relative to this dataset directory
fps	Playback rate, always 1
n_frames	Number of frames in the released MP4
structure	Question structure
difficulty	Difficulty label
num_hops	Annotated reasoning hop count
knowledge_domain	Knowledge category

Multiple questions may reference the same video. media.jsonl contains one entry per unique MP4 with its relative path, byte size, frame count, playback rate and encoded dimensions. frames.jsonl maps each video path to its ordered original-resolution PNGs, with relative paths, frame indices, byte sizes and image dimensions. alignment.json records the scope and results of the frame correspondence checks.

The checks cover all 307 QA records, 565 annotated target-frame references, 293 videos and 37,354 PNGs. Annotation frame indices match their source-frame mapping. Encoding the ordered reference PNGs reproduces 253 MP4s byte for byte; the other 40 videos pass per-frame visual correspondence checks against all candidate frame slots. See alignment.json for the comparison method and tolerances. All distributed PNGs are byte-identical to their reference files.

Optional MP4 frame extraction

The benchmark uses the distributed original-resolution PNGs. For inspection, extract_frames.py can also decode frames from the MP4 files:

python extract_frames.py
python extract_frames.py --video media/0000.mp4
python extract_frames.py --output-dir ./decoded_frames --workers 4

These diagnostic frames go to runtime/decoded_frames/<video_id>/ by default. The script preserves the MP4 frame order and encoded dimensions, which can be smaller than the benchmark PNG dimensions. It refuses to write to the official runtime/frames/ tree. MP4-decoded images must not replace the original-resolution benchmark images. No API key or network access is needed for extraction.

Prepare inference inputs

Requirements: Python 3.9 or newer, ffmpeg and ffprobe on PATH. The loader uses the Python standard library and makes no model or judge calls.

After extracting both kinds of media archives, check all videos and PNGs:

python prepare.py --verify-only

Expose this entire dataset directory through an HTTP(S) media server reachable by the inference backend. Set MEDIA_BASE_URL to its URL, then run:

python prepare.py --media-base-url "$MEDIA_BASE_URL"

The loader checks each MP4 byte size, stream layout, playback rate and frame count, then checks the existing original-resolution PNG filenames, sequence, byte sizes and dimensions against frames.jsonl. It does not decode, resize, replace or regenerate the benchmark images. Missing or mismatched frame files cause an error instead of falling back to MP4 decoding.

The generated files are:

• runtime/video.csv: index,question,answer for inference and evaluation.
• runtime/video_manifest.jsonl: the video and ordered frame URLs for tools. The media server must also expose runtime/frames/, populated by extracting the frame archives. The loader does not generate these PNGs.

For every sample, case_id equals its id; n_frames equals the number of frame_urls; frame_indices is 0..n_frames-1; grid_fps is 1.0; duration is n_frames playback seconds. Frame slot i refers to the frame at playback time i seconds. A time span [a, b) uses slots a through b-1. The manifest contains no answers, annotations or source-video metadata.

From the OneSearch-VL code repository, with DATA_ROOT pointing to this dataset:

python onesearch_vl/run_infer_video.py \
  --model 8b --checkpoint /models/your-checkpoint \
  --csv "$DATA_ROOT/runtime/video.csv" \
  --manifest "$DATA_ROOT/runtime/video_manifest.jsonl" \
  --output-dir outputs/onesearch_video

bash onesearch_vl/run_eval.sh video \
  --csv "$DATA_ROOT/runtime/video.csv" \
  --traj_dir outputs/onesearch_video

Configure inference, search and evaluation credentials in the code repository before running those commands. Only the question and visual media are actor inputs. Reference answers are used by the evaluator. Audio, source timestamps, source URLs and video metadata are not supplied as evidence.

The MP4 overview and the PNG tool inputs represent the same ordered frame sequence. The MP4 is scaled and compressed; the PNGs retain the annotation images' resolution for object crops and visual search. Frame slot i in the video maps to frame_i in the PNG sequence. A correspondence check does not establish the semantic correctness or answerability of every QA sample. No model evaluation results are included in this package.