README.md

---
pretty_name: TomaMMU
license: cc-by-nc-4.0
task_categories:
- image-text-to-text
- visual-question-answering
size_categories:
- 100K<n<1M
source_datasets:
- LeafNet
- TOM2024
- In-house tomato leaf images (TomaMMU authors)
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_bytes: 334409642
    num_examples: 213119
  download_size: 334409642
  dataset_size: 334409642
tags:
- agriculture
- vision-language
- vqa
- agvqa
- tomammu
- tomato
---

# TomaMMU

![TomaMMU sample images](static/TomaMMU_sample.webp)

TomaMMU is a visual question answering dataset about tomato leaf diseases. It has 213,119 question and answer pairs over 28,808 leaf images in 15 categories (14 diseases and healthy), covering 25 disease symptoms. The images come from LeafNet, TOM2024 and the authors' own collections, and include both field and lab photos. Human annotators reviewed and finalized every question and answer.

The questions cover 7 tasks at 3 levels. The authors tested 14 vision-language models; the best zero-shot model, Gemini 2.0 Flash, averaged 70.3% on the multiple-choice questions. Their fine-tuned model, TomaLLaVA, reached 96.09%.

A second paper from the same group trains SOLAR, a model for tomato leaf question answering, using this data.

- Paper: [TomaMMU: A Comprehensive Multimodal Understanding Benchmark for Tomato Leaf Diseases](https://arxiv.org/abs/2608.08727)
- Additional paper: [A Multi-Modal Generative Model for Tomato Disease Leaves Understanding](https://arxiv.org/abs/2609.19555)
- Original dataset: [enalis/TomaMMU](https://huggingface.co/datasets/enalis/TomaMMU)

## Tasks

| Level | Task | `question_type` |
|---|---|---|
| 1. Basic Perception | Healthy-Diseased Classification | `HDC` |
| 1. Basic Perception | Leaf Classification | `LC` |
| 1. Basic Perception | Crop Species Identification | `CSI` |
| 2. Pathology Understanding | Symptom Identification | `SI` |
| 2. Pathology Understanding | Pathogen Classification | `PC` |
| 3. Expert Diagnosis | Disease Classification | `DC` |
| 3. Expert Diagnosis | Scientific Name Classification | `SNC` |

All rows are multiple-choice, with 2 or 4 options.

## 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`: `tomammu_train_<counter>`, for example `tomammu_train_0`.
- `messages`: one user turn (image, then question with options) and one assistant turn (answer).
- `raw_metadata`: JSON string with `image_path`, `question_id` and `question_type`.

Example:

```json
{
  "images": [{"bytes": null, "path": "4a31a8b625ce2e767cd4ed8e54f9b7d0bf0e9fb843e16050b7679a6bc8f293c8"}],
  "id": "tomammu_train_0",
  "messages": [
    {"role": "user", "content": [
      {"type": "image", "text": null},
      {"type": "text", "text": "Does the image show a diseased plant?\nA. Disease\nB. Healthy"}
    ]},
    {"role": "assistant", "content": [{"type": "text", "text": "A. Disease"}]}
  ],
  "raw_metadata": "{\"image_path\": \"val/Tomato___Bacterial_Floundering/1005963105_1668687142186_640x480.jpg\", \"question_id\": \"hdc::00001\", \"question_type\": \"HDC\"}"
}
```

### Changes from the original data

- Built from `Tomato.csv`, which has one row per question. The source has no split column, so all rows are in one `train` split. The paper's TomaBench split is not reproduced.
- Options come from the `A` to `D` columns and are added to the question as lines like `A. text`. Empty options are left out, so some questions show only A and B. Spaces around option text are trimmed.
- Answers combine the answer letter and the `correct_text` column, for example `A. Disease`.
- `raw_metadata` keeps `image_path`, `question_id` and `question_type`. The question, options and answer are already in `messages`.
- The CSV has 36,993 different image path strings, but many differ only in letter case and point to the same file. Images are stored once, deduplicated by SHA256, which leaves 28,112 unique images. Only images used by the rows are packed.

## Usage

```python
from agml import loadImageTextToTextDataset

dataset = loadImageTextToTextDataset("Project-AgML/TomaMMU")

sample = dataset["train"][0]
sample["images"]    # list of PIL images
sample["messages"]
```

## Citation

```bibtex
@article{truong2026tomammu,
  title={TomaMMU: A Comprehensive Multimodal Understanding Benchmark for Tomato Leaf Diseases},
  author={Truong, Gia-Han and Quoc, Khang Nguyen and Quach, Luyl-Da},
  journal={arXiv preprint arXiv:2608.08727},
  year={2026}
}

@article{quoc2026solar,
  title={A Multi-Modal Generative Model for Tomato Disease Leaves Understanding},
  author={Quoc, Khang Nguyen and Tran, Minh-Phuoc and Truong, Gia-Han and Quach, Luyl-Da},
  journal={arXiv preprint arXiv:2609.19555},
  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.