README.md

metadata

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

OneSearch-MI

OneSearch-MI contains 301 questions and 910 unique PNG images. Each question uses multiple images in a fixed order. The images retain their original bytes, resolution and aspect ratio; no resizing or recompression is applied.

Files

README.md
prepare.py
test.jsonl
media.jsonl
media/
  0000.png
  ...

Media is distributed in mi-00001.zip. Extract all media archives into this directory, preserving their paths. Each archive is at most 10 GB (10,000,000,000 bytes). All images needed by this benchmark are included; the video benchmark is not required.

test.jsonl contains 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
images	Ordered image paths relative to this MI dataset directory
n_images	Number of images in this sample
structure	Question structure
difficulty	Difficulty label
num_hops	Annotated reasoning hop count
knowledge_domain	Knowledge category

Preserve the order of images. The first list element becomes img_1, the second becomes img_2, and so on. Numeric filenames are media identifiers, not the input sequence; sorting the filenames can change the sample. Multiple questions may share an image. media.jsonl has one entry per unique PNG, including its relative path, byte size, width and height.

Prepare inference inputs

The loader requires Python 3.9 or newer and uses only the standard library. It makes no model or judge calls and does not modify the images.

Check image byte sizes, PNG dimensions and sample references:

python prepare.py --verify-only

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

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

This produces:

• runtime/mi.csv: index,question,answer for inference and evaluation.
• runtime/mi_manifest.jsonl: ordered image URLs for the multi-image pipeline.

Each manifest record has case_id equal to the sample id, ok: true, modality: "multi_image", n_images, and ordered image_urls. It contains no reference answer, source-video metadata or annotations. The question is loaded from the CSV; the reference answer is reserved for evaluation.

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

python onesearch_vl/run_infer_mi.py \
  --model 8b --checkpoint /models/your-checkpoint \
  --csv "$DATA_ROOT/runtime/mi.csv" \
  --manifest "$DATA_ROOT/runtime/mi_manifest.jsonl" \
  --output-dir outputs/onesearch_mi

bash onesearch_vl/run_eval.sh image \
  --csv "$DATA_ROOT/runtime/mi.csv" \
  --traj_dir outputs/onesearch_mi

Configure inference, search and evaluation credentials in the code repository before running those commands. No evaluation results are included in this package.