Add files using upload-large-folder tool

README.md

@@ -1,3 +1,49 @@
 ---
+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
+```

conversion_stats.json

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+{
+  "seen": 4342,
+  "written": 4342,
+  "text_only": 0,
+  "dropped": 0,
+  "dropped_breakdown": {
+    "skipped_no_shape": 0,
+    "skipped_bad_image": 0,
+    "skipped_passthrough_invalid": 0
+  },
+  "coverage": 1.0,
+  "images_copied": 4342,
+  "rows_by_shape": {
+    "shape1": 2482,
+    "shape2": 1860
+  },
+  "subsets": [
+    "png"
+  ]
+}