| --- |
| configs: |
| - config_name: default |
| data_files: |
| - "metadata.jsonl" |
| - "images/**" |
| license: cc-by-nc-4.0 |
| task_categories: |
| - image-text-to-text |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # AgroBench |
|
|
| AgroBench is a vision-language model (VLM) benchmark for agriculture, annotated by expert agronomists. It covers seven agricultural topics spanning 203 crop categories and 682 disease categories, with 4,342 question-answer examples pairing images with multiple-choice questions across tasks such as crop identification, disease diagnosis, pest identification, and weed identification. |
|
|
| This dataset has been standardized to the HF `image_text_to_text` format: one conversational `messages` schema, imagefolder-native images, and (if present) a `text_only` parquet config. |
|
|
| This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Single image folder -> one default config |
| ds = load_dataset("Project-AgML/AgroBench") |
| |
| # Stream without downloading |
| ds = load_dataset("Project-AgML/AgroBench", streaming=True) |
| ``` |
|
|
| Every record shares the SAME columns so heterogeneous datasets concatenate cleanly: id, file_names (1..N images), messages, origin_dataset, and raw_metadata. raw_metadata is a JSON-encoded string holding every original source field that was NOT already folded into messages/file_names (the question, answer, options, and image paths are omitted to avoid duplication), preserved verbatim, or {} if none remain; restore them with json.loads(row["raw_metadata"]). Using a JSON string (not a native struct) is what lets concatenate_datasets([...]) work across datasets whose raw fields differ in type. Multi-image rows return images as a list aligned to the {"type": "image"} placeholders in messages. |
| |
| # Citation |
| |
| ```bibtex |
| @InProceedings{Shinoda_2025_ICCV, |
| author = {Shinoda, Risa and Inoue, Nakamasa and Kataoka, Hirokatsu and Onishi, Masaki and Ushiku, Yoshitaka}, |
| title = {AgroBench: Vision-Language Model Benchmark in Agriculture}, |
| booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, |
| month = {October}, |
| year = {2025}, |
| pages = {7634-7644} |
| } |
| |
| Shinoda, Risa; Inoue, Nakamasa; Kataoka, Hirokatsu; Onishi, Masaki; Ushiku, Yoshitaka (2025), "AgroBench: Vision-Language Model Benchmark in Agriculture", Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 7634-7644 |
| ``` |
