Updated AgroOmni dataset with sample images and README.md changes

README.md

@@ -1,196 +1,218 @@
 ---
 pretty_name: MIRAGE
-dataset_info:
+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
-  splits:
-  - name: train
-    num_examples: 17532
-  - name: test
-    num_examples: 8184
+  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
-  splits:
-  - name: test
-    num_examples: 3934
+  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
-configs:
+  download_size: 1326686099
+  dataset_size: 1326686099
 - config_name: MMST_Standard
-  data_files:
-  - split: train
-    path: MMST_Standard/train-*.parquet
-  - split: test
-    path: MMST_Standard/test-*.parquet
+  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
-  data_files:
-  - split: test
-    path: MMST_Contextual/test-*.parquet
-- config_name: MMMT_Direct
-  default: true
-  data_files:
-  - split: train
-    path: MMMT_Direct/train-*.parquet
-  - split: dev
-    path: MMMT_Direct/dev-*.parquet
-  - split: test
-    path: MMMT_Direct/test-*.parquet
-- config_name: MMMT_Decomp
-  data_files:
-  - split: train
-    path: MMMT_Decomp/train-*.parquet
-  - split: dev
-    path: MMMT_Decomp/dev-*.parquet
-  - split: test
-    path: MMMT_Decomp/test-*.parquet
-license: cc-by-sa-4.0
-task_categories:
-- image-text-to-text
-- visual-question-answering
-language:
-- en
-tags:
-- vqa
-- agriculture
-- agml
-- vision-language
-- mirage
-size_categories:
-- 100K<n<1M
+  features: *features
+  splits:
+  - name: test
+    num_bytes: 6599964
+    num_examples: 3934
+  download_size: 1341825286
+  dataset_size: 1341825286
 ---
 
 # MIRAGE
 
-MIRAGE is a benchmark for multimodal, expert-guided agricultural consultations,
-introduced in ["MIRAGE: A Benchmark for Multimodal Information-Seeking and
-Reasoning in Agricultural Expert-Guided Conversations"](https://arxiv.org/abs/2506.20100)
-(arXiv:2506.20100, NeurIPS 2025). It's built from real crop health and pest
-diagnosis consultations — curated down from 218,000 raw interactions
-(2012–2025) to 37,512 high-quality ones, spanning 7,600+ distinct biological
-entities (plants, pests, diseases). Unlike closed-set VQA benchmarks, MIRAGE's
-scenarios are underspecified and open-world by design, requiring models to
-recognize when they need more context rather than guessing. In the paper's
-evaluation, 20+ frontier vision-language models were tested, with the
-best (GPT-4.1) reaching only 43.9% identification accuracy.
+![MIRAGE sample images](static/MIRAGE_sample.webp)
 
-## Configs
+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.
 
-MIRAGE ships as four configs, corresponding to the paper's task families:
+The authors tested 22 vision-language models. Even the best, GPT-4.1, reached only 43.9% identification accuracy.
 
-| config | description | splits |
-|---|---|---|
-| `MMST_Standard` | Single-turn, self-contained questions answerable from the given text + image(s) alone — entity identification, causal explanation, visually-grounded answers. | train, test |
-| `MMST_Contextual` | Single-turn questions with a latent information gap — the model must recognize missing context (location, time, agricultural background) rather than answer directly. | test |
-| `MMMT_Direct` **(default)** | Multi-turn dialogues; at each turn the model must decide whether to ask a clarifying question or answer, and generate that utterance directly. | train, dev, test |
-| `MMMT_Decomp` | The same underlying multi-turn dialogues as `MMMT_Direct`, reformulated as a decomposed reasoning task (hence the matching unique-image counts between the two configs below). | train, dev, test |
+- 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)
 
-`MMMT_Direct` is set as the default config.
+## Configs
 
-## Layout
+| 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 |
 
-Each config is a self-contained folder — its own metadata parquet(s) plus its
-own `images/` folder of zip shards — following the same storage format across
-every AgML-standardized dataset:
+## Structure
 
 ```
-MIRAGE_P/
-  MMST_Standard/
-    train-0000-of-0001.parquet   # 17,532 rows
-    test-0000-of-0001.parquet    #  8,184 rows
-    images/
-      image-mmst_standard-000-of-003.zip ... 002   # 46,874 unique images, 6.92GB
-      path_to_shard.parquet
-  MMST_Contextual/
-    test-0000-of-0001.parquet    #  3,934 rows
-    images/
-      image-mmst_contextual-000-of-001.zip         #  8,032 unique images, 1.33GB
-      path_to_shard.parquet
-  MMMT_Direct/
-    train-0000-of-0001.parquet   #  3,876 rows
-    dev-0000-of-0001.parquet     #    878 rows
-    test-0000-of-0001.parquet    #    861 rows
-    images/
-      image-mmmt_direct-000-of-001.zip             #  7,995 unique images, 1.32GB
-      path_to_shard.parquet
-  MMMT_Decomp/
-    train-0000-of-0001.parquet   #  3,876 rows
-    dev-0000-of-0001.parquet     #    878 rows
-    test-0000-of-0001.parquet    #    861 rows
-    images/
-      image-mmmt_decomp-000-of-001.zip             #  7,995 unique images, 1.32GB
-      path_to_shard.parquet
+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\"}"
+}
 ```
 
-Every row has: `images`, `id`, `messages`, `raw_metadata`. `images` holds
-`{"bytes": None, "path": ...}` per referenced image — but unlike AgroOmni,
-`path` here is a **SHA256 content hash**, not a filesystem path: MIRAGE's
-source data has no path metadata at all (images exist only as embedded bytes
-per row), so the hash is what makes dedup and shard lookup possible.
-`path_to_shard.parquet` maps each unique hash to the shard filename holding
-it. `answer_type` (mcqa/oeqa) lives inside `raw_metadata` rather than as its
-own column, keeping the schema consistent with every other AgML dataset.
-
-**Not auto-loadable via `load_dataset()`.** Requires the companion loader
-(`load_agml_dataset()`), which reads images lazily and directly out of the
-zip shards — no extraction step.
-
-## Why content hashing instead of paths
-
-The companion loader looks up each image independently via
-`zipfile.ZipFile.open(member_name)` — true random access via zip's central
-directory, no extraction needed — so, as with AgroOmni, there's no
-correctness requirement that a row's co-referenced images share a shard.
-Since MIRAGE has no natural path identifiers, a SHA256 hash of each image's
-raw bytes is used instead, which has the added benefit of deduplicating any
-images that repeat across rows within a config.
+### 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
 
-ds = loadImageTextToTextDataset(
-      "Project-AgML/MIRAGE",
-      config="MMMT_Direct",
-      token=HF_TOKEN
-)
-print(ds)                 # DatasetDict({'train': ..., 'dev': ..., 'test': ...})
-ds["train"][0]            # images decoded lazily on access
+dataset = loadImageTextToTextDataset("Project-AgML/MIRAGE", config="MMST_Standard")
+
+sample = dataset["test"][0]
+sample["images"]    # list of PIL images
+sample["messages"]
 ```
 
 ## Citation
 
-If you use this dataset, please cite the original MIRAGE paper:
-
 ```bibtex
-@misc{mirage2025,
-  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.},
-  year   = {2025},
-  eprint = {2506.20100},
-  archivePrefix = {arXiv},
-  primaryClass  = {cs.CL},
-  note   = {NeurIPS 2025},
-  url    = {https://arxiv.org/abs/2506.20100}
+@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}
 }
 ```
 
-Project page: https://mirage-benchmark.github.io/
-
-Code: https://github.com/MIRAGE-Benchmark/MIRAGE-Benchmark
-
----
-
-This dataset is indexed and structured on https://project-agml.github.io/ as part of the AgML python library. 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.
+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.