| --- |
| 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 |
|
|
| ```text |
| 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: |
|
|
| ```bash |
| 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: |
|
|
| ```bash |
| python prepare.py --media-base-url "$MEDIA_BASE_URL" |
| ``` |
|
|
| This produces: |
|
|
| - `runtime/mi.csv`: `index,question,answer` for inference and evaluation. |
| - `runtime/mi_manifest.jsonl`: ordered image URLs for the multi-image pipeline. |
|
|
| Each manifest record has `case_id` equal to the sample `id`, `ok: true`, |
| `modality: "multi_image"`, `n_images`, and ordered `image_urls`. It contains |
| no reference answer, source-video metadata or annotations. The question is |
| loaded from the CSV; the reference answer is reserved for evaluation. |
|
|
| From the OneSearch-VL code repository, with `DATA_ROOT` pointing to this MI |
| dataset directory: |
|
|
| ```bash |
| python onesearch_vl/run_infer_mi.py \ |
| --model 8b --checkpoint /models/your-checkpoint \ |
| --csv "$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. |
|
|
