Uploading standardized LeafBench dataset with image shards and metadata files

README.md

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+---
+pretty_name: LeafBench
+license: other
+task_categories:
+- image-text-to-text
+- visual-question-answering
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train-0000-of-0001.parquet
+dataset_info:
+  features:
+  - name: images
+    list:
+    - name: bytes
+      dtype: 'null'
+    - name: path
+      dtype: string
+  - name: id
+    dtype: string
+  - name: messages
+    list:
+    - name: role
+      dtype: string
+    - name: content
+      list:
+      - name: type
+        dtype: string
+      - name: text
+        dtype: string
+  - name: raw_metadata
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 13950
+---
+
+# LeafBench
+
+LeafBench is a multiple-choice visual question answering benchmark for testing how well vision-language models understand plant diseases. It was built from LeafNet, a dataset of about 186,000 leaf images from 22 crop species in 97 classes, collected in 7 countries. Every question asks about the leaf in the image and gives a closed set of answers, so the model must pick one instead of writing free text.
+
+The questions cover 6 tasks, from telling healthy and diseased leaves apart to naming the disease, the crop, the pathogen type, the pathogen's scientific name and the main symptom. The authors tested 12 vision-language models zero-shot. Healthy or diseased questions were easy, with the best models above 90%, but pathogen and scientific name questions stayed below 65% even for frontier models. On the dataset card, GPT-4o averages 72.78%, SCOLD (the authors' own model trained on LeafNet) 72.38% and Gemini 2.5 Pro 69.18%.
+
+- Paper: [LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases](https://arxiv.org/abs/2602.13662)
+- Original dataset: [enalis/LeafBench](https://huggingface.co/datasets/enalis/LeafBench)
+- Image and caption dataset: [Project-AgML/LeafNet](https://huggingface.co/datasets/Project-AgML/LeafNet)
+- Evaluation code: [EnalisUs/LeafBench](https://github.com/EnalisUs/LeafBench)
+- License: the original dataset card is not consistent. Its metadata says `mit` and its text says CC BY 4.0. Access is gated, and users must agree to use the data for non-commercial purposes only (academic research or education).
+
+## Tasks
+
+| Task | `question_type` | Rows |
+|---|---|---|
+| Healthy-Diseased Classification | `HDC` | 2,580 |
+| Disease Identification | `DC` | 2,580 |
+| Crop Species Identification | `CSI` | 2,580 |
+| Scientific Name Classification | `SNC` | 2,070 |
+| Pathogen Classification | `PC` | 2,070 |
+| Symptom Identification | `SI` | 2,070 |
+
+The dataset card calls Disease Identification `DI`, but the data uses `DC`. Healthy-Diseased questions have two options (Yes or No). All other questions have four.
+
+## Structure
+
+```
+train-0000-of-0001.parquet
+images/
+  image-train-000-of-001.zip
+  path_to_shard.parquet
+```
+
+Each row has:
+
+- `images`: list with one `{"bytes": null, "path": <sha256>}`. The path is the SHA256 of the image bytes and points into a zip shard through `images/path_to_shard.parquet`.
+- `id`: `leafbench_train_<counter>`, for example `leafbench_train_0`.
+- `messages`: one user turn (image, then question with options) and one assistant turn (answer).
+- `raw_metadata`: JSON string with `question_id`, `question_type` and `raw_image_paths`.
+
+Example:
+
+```json
+{
+  "images": [{"bytes": null, "path": "37a342d681b968f2df2c18298560f39059aeed3fd6beae378addab30fd0076cd"}],
+  "id": "leafbench_train_0",
+  "messages": [
+    {"role": "user", "content": [
+      {"type": "image", "text": null},
+      {"type": "text", "text": "Does the image show a diseased plant?\nA. Yes\nB. No"}
+    ]},
+    {"role": "assistant", "content": [{"type": "text", "text": "A. Yes"}]}
+  ],
+  "raw_metadata": "{\"question_id\": \"hdc::1\", \"question_type\": \"HDC\", \"raw_image_paths\": [\"f3ff804e-bca4-4a23-9af9-1f5b8c5c9980___JR_FrgE.S 3053.JPG\"]}"
+}
+```
+
+### Changes from the original data
+
+- Built from the 6 parquet files in `data/`. All 13,950 rows are kept in the same order. The source has one `test` split, which is stored here as `train`.
+- Options come from the `A` to `D` columns and are added to the question as lines like `A. text`. Missing options (C and D in Healthy-Diseased questions) are left out. All text is kept exactly as written, including a leading space in the Scientific Name question and extra spaces in some options.
+- Answers combine the answer letter and its option text, for example `A. Yes`.
+- `raw_metadata` keeps `question_id` and `question_type`. The image's own path from the source parquet is kept in `raw_image_paths`. The question, options and answer are already in `messages`.
+- The images were stored inside the source parquet files, once per question. They are moved into a zip shard and stored once, deduplicated by SHA256. The rows name 2,506 different image files, which hold 2,535 unique images, because some different images share a file name. The dataset card says 2,910 images and the paper says 2,570.
+- The tiny subset in `leafbench_tiny.csv` (880 questions) is not included. All of its questions are also in the full benchmark.
+
+## Usage
+
+```python
+from agml import loadImageTextToTextDataset
+
+dataset = loadImageTextToTextDataset("Project-AgML/LeafBench")
+
+sample = dataset["train"][0]
+sample["images"]    # list of PIL images
+sample["messages"]
+```
+
+## Citation
+
+```bibtex
+@article{quoc2026leafnet,
+  title={LeafNet: A Large-Scale Dataset and Comprehensive Benchmark for Foundational Vision-Language Understanding of Plant Diseases},
+  author={Quoc, Khang Nguyen and Dao, Phuong D and Quach, Luyl-Da},
+  journal={arXiv preprint arXiv:2602.13662},
+  year={2026}
+}
+```
+
+This dataset is indexed and structured on https://project-agml.github.io/ as part of Project AgML @ UC Davis. This dataset was reformatted from its original format to match HuggingFace's Imagefolder standards but requires an external module (agml) that processes and returns a HF Dataset object faster than HF module functions.

images/image-train-000-of-001.zip

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images/path_to_shard.parquet

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train-0000-of-0001.parquet

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