| --- |
| pretty_name: MIRAGE |
| license: cc-by-sa-4.0 |
| task_categories: |
| - image-text-to-text |
| - visual-question-answering |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - AskExtension |
| configs: |
| - config_name: MMMT_Direct |
| default: true |
| data_files: |
| - split: train |
| path: MMMT_Direct/train-0000-of-0001.parquet |
| - split: dev |
| path: MMMT_Direct/dev-0000-of-0001.parquet |
| - split: test |
| path: MMMT_Direct/test-0000-of-0001.parquet |
| - config_name: MMMT_Decomp |
| data_files: |
| - split: train |
| path: MMMT_Decomp/train-0000-of-0001.parquet |
| - split: dev |
| path: MMMT_Decomp/dev-0000-of-0001.parquet |
| - split: test |
| path: MMMT_Decomp/test-0000-of-0001.parquet |
| - config_name: MMST_Standard |
| data_files: |
| - split: train |
| path: MMST_Standard/train-0000-of-0001.parquet |
| - split: test |
| path: MMST_Standard/test-0000-of-0001.parquet |
| - config_name: MMST_Contextual |
| data_files: |
| - split: test |
| path: MMST_Contextual/test-0000-of-0001.parquet |
| dataset_info: |
| - config_name: MMMT_Direct |
| features: &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: 3734905 |
| num_examples: 3876 |
| - name: dev |
| num_bytes: 844393 |
| num_examples: 878 |
| - name: test |
| num_bytes: 844960 |
| num_examples: 861 |
| download_size: 1325714410 |
| dataset_size: 1325714410 |
| - config_name: MMMT_Decomp |
| features: *features |
| splits: |
| - name: train |
| num_bytes: 4407679 |
| num_examples: 3876 |
| - name: dev |
| num_bytes: 995362 |
| num_examples: 878 |
| - name: test |
| num_bytes: 992906 |
| num_examples: 861 |
| download_size: 1326686099 |
| dataset_size: 1326686099 |
| - config_name: MMST_Standard |
| features: *features |
| splits: |
| - name: train |
| num_bytes: 17348634 |
| num_examples: 17532 |
| - name: test |
| num_bytes: 7962774 |
| num_examples: 8184 |
| download_size: 6948107240 |
| dataset_size: 6948107240 |
| - config_name: MMST_Contextual |
| features: *features |
| splits: |
| - name: test |
| num_bytes: 6599964 |
| num_examples: 3934 |
| download_size: 1341825286 |
| dataset_size: 1341825286 |
| --- |
| |
| # MIRAGE |
|
|
|  |
|
|
| MIRAGE is a benchmark built from real conversations between farmers or gardeners and agricultural experts on the AskExtension platform. The authors collected about 285,000 consultations from 2012 to 2025 and kept 37,512 high-quality ones, covering more than 7,600 plants, pests and diseases. Many questions leave out key details, so a good model has to notice what is missing and ask, not just answer. |
|
|
| The authors tested 22 vision-language models. Even the best, GPT-4.1, reached only 43.9% identification accuracy. |
|
|
| - Paper: [MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations](https://arxiv.org/abs/2506.20100) (NeurIPS 2025 Datasets and Benchmarks) |
| - Project page: [mirage-benchmark.github.io](https://mirage-benchmark.github.io/) |
| - Original dataset: [MIRAGE-Benchmark/MIRAGE](https://huggingface.co/datasets/MIRAGE-Benchmark/MIRAGE) |
|
|
| ## Configs |
|
|
| | Config | What it is | Train | Dev | Test | |
| |---|---|---|---|---| |
| | `MMST_Standard` | Single-turn questions that can be answered from the text and images | 17,532 | | 8,184 | |
| | `MMST_Contextual` | Single-turn questions with missing context (place, time or background) the model should notice | | | 3,934 | |
| | `MMMT_Direct` | Multi-turn dialogues where the model decides to ask a clarifying question or answer, then writes it | 3,876 | 878 | 861 | |
| | `MMMT_Decomp` | The same dialogues, with the known and missing goals spelled out | 3,876 | 878 | 861 | |
|
|
| ## Structure |
|
|
| ``` |
| MMST_Standard/ |
| train-0000-of-0001.parquet |
| test-0000-of-0001.parquet |
| images/ |
| image-mmst_standard-000-of-003.zip ... image-mmst_standard-002-of-003.zip |
| path_to_shard.parquet |
| MMST_Contextual/ |
| test-0000-of-0001.parquet |
| images/ |
| MMMT_Direct/ |
| train-0000-of-0001.parquet |
| dev-0000-of-0001.parquet |
| test-0000-of-0001.parquet |
| images/ |
| MMMT_Decomp/ |
| (same as MMMT_Direct) |
| Source_Data_For_MMMT.json |
| ``` |
|
|
| Each config has its own `images/` folder. Unique images per config: `MMST_Standard` 46,874, `MMST_Contextual` 8,032, `MMMT_Direct` and `MMMT_Decomp` 7,995 each. |
|
|
| Each row has: |
|
|
| - `images`: list of `{"bytes": null, "path": <sha256>}`, up to 3 per row, in the source's `image_1`, `image_2`, `image_3` order. The path is the SHA256 of the image bytes and points into a zip shard through that config's `images/path_to_shard.parquet`. |
| - `id`: `mirage_<config>_<split>_<counter>`, for example `mirage_mmst_standard_test_0`. |
| - `messages`: one user turn (images, then text) and one assistant turn. |
| - In the MMST configs, the text is the question and the assistant turn is the expert's answer. |
| - In the MMMT configs, the text is the dialogue so far (`dialog_context`, with `user:` and `expert:` lines), and the assistant turn is the next expert utterance. |
| - `raw_metadata`: JSON string. |
| - MMST: `original_id`, `subset`, `answer_type`, `category`, `entity_type`, `entity_name`, `entity_scientific_name`, `entity_common_names`, `state`, `county` and `asked_time`. `MMST_Contextual` also has `location_related`, `time_related`, `location_related_analysis` and `time_related_analysis`. |
| - MMMT: `original_id`, `subset`, `answer_type`, `decision` (such as `<Clarify>`), `dialog_turns`, `known_goal` and `missing_goal`. |
|
|
| Example (texts shortened): |
|
|
| ```json |
| { |
| "images": [{"bytes": null, "path": "cbff90a5ba89a76488f0533e5a98dc2a33095785a299e90eb7c09664a058e773"}], |
| "id": "mirage_mmst_standard_test_0", |
| "messages": [ |
| {"role": "user", "content": [ |
| {"type": "image", "text": null}, |
| {"type": "text", "text": "My arborvitae's have little gold, looking seeds on the ends and are also turning brown. Part of it m..."} |
| ]}, |
| {"role": "assistant", "content": [{"type": "text", "text": "The seeds are just that. Just like some get pine cones, this is the arborvitae's..."}]} |
| ], |
| "raw_metadata": "{\"original_id\": \"#885475\", \"subset\": \"MMST_Standard\", \"answer_type\": \"oeqa\", \"category\": \"Plant Care and Gardening Guidance\", \"entity_type\": \"plant\", \"entity_name\": \"arborvitae\", \"entity_scientific_name\": \"\", \"entity_common_names\": [], \"state\": \"Minnesota\", \"county\": \"Stearns County\", \"asked_time\": \"2024-09-16 16:35:00\"}" |
| } |
| ``` |
|
|
| ### Changes from the original data |
|
|
| - Each source config becomes a config here, with its own source splits. `dev` keeps its source name. |
| - `image_1`, `image_2` and `image_3` become the `images` list. Empty slots are left out. |
| - The MMMT dialogue history stays as one block of text in the user turn, exactly as in `dialog_context`. It is not split into separate turns. |
| - Some source fields are renamed in `raw_metadata`: `id` becomes `original_id`, and `meta_data_state`, `meta_data_county` and `meta_data_asked_time` become `state`, `county` and `asked_time`. |
| - `raw_metadata` also has two fields that are not in the source: |
| - `subset`: the config name. |
| - `answer_type`: added during conversion. It is `mcqa` when the question lists at least two lettered or numbered options, or the answer is a single letter or digit, and `oeqa` otherwise. MMMT rows are always `oeqa`. |
| - `MMMT_Direct` rows have `known_goal` and `missing_goal` set to null, since only `MMMT_Decomp` has them in the source. |
| - Images are stored once per config, deduplicated by SHA256. `MMMT_Direct` and `MMMT_Decomp` share the same images, so each holds its own copy. |
| - `Source_Data_For_MMMT.json` from the original dataset is included unchanged. |
|
|
| ## Usage |
|
|
| ```python |
| from agml import loadImageTextToTextDataset |
| |
| dataset = loadImageTextToTextDataset("Project-AgML/MIRAGE", config="MMST_Standard") |
| |
| sample = dataset["test"][0] |
| sample["images"] # list of PIL images |
| sample["messages"] |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{dongre2025mirage, |
| title={MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations}, |
| author={Dongre, Vardhan and Gui, Chi and Garg, Shubham and Nayyeri, Hooshang and Tur, Gokhan and Hakkani-T{\"{u}}r, Dilek and Adve, Vikram S}, |
| journal={arXiv preprint arXiv:2506.20100}, |
| year={2025} |
| } |
| ``` |
|
|
| 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. |
