Uploading AgroOmni dataset with image shards and metadata parquet files

README.md

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+---
+pretty_name: AgroOmni
+license: other
+task_categories:
+- image-text-to-text
+- visual-question-answering
+size_categories:
+- 100K<n<1M
+source_datasets:
+- ACFR Orchard Fruit Dataset
+- 2018 AI Challenge Dataset
+- IP102
+- Agriculture Vision Challenge
+- OilPalmUAV
+- CropHarvest
+- GWHD
+- CLCD
+- PhenoBench
+- Tomato Dataset
+- OAM-TCD
+- EarthVQA
+- Maize Tassel Identification Dataset
+- Private Parcel Dataset
+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: 42703714964
+    num_examples: 288831
+  download_size: 42703714964
+  dataset_size: 42703714964
+---
+
+# AgroOmni
+
+![AgroOmni sample images](static/AgroOmni_sample.webp)
+
+AgroOmni is a large agricultural training set for vision-language models that mixes three views: ground photos, UAV (drone) images and satellite images. Most models only learn from ground-level photos and get confused at other scales, for example mistaking farmland for a wall or floor. AgroOmni has 288,831 question and answer pairs over 107,488 images, in 56 task categories across 14 task types.
+
+By pair count, 68.0% of the data is ground view, 24.1% satellite and 7.9% UAV. The pairs were made from the source labels with rule-based templates and with logic built from the raw labels, then filtered and checked by hand. The data was also cleaned against the AgroMind benchmark so the two do not overlap.
+
+The authors trained AgroNVILA on it. It scored 62.32% on AgroMind, 15.03 points above GPT-5.2.
+
+- Paper: [AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning](https://arxiv.org/abs/2603.14342)
+- Original dataset: [anonymous7777/AgroOmni](https://huggingface.co/datasets/anonymous7777/AgroOmni)
+- License: the original dataset card marks it as `other` and does not name a license.
+
+## Tasks
+
+| Dimension | Task types |
+|---|---|
+| Spatial Perception | Spatial Localization, Spatial Relationship, Boundary Detection |
+| Object Understanding | Object Classification, Pest/Disease Diagnostics, Growth Status Recognition |
+| Scene Understanding | Counting, Area Statistics, Scene Comparison, Temporal Variation |
+| Scene Reasoning | Visual Prompt Reasoning, Anomaly Reasoning, Climate Type Reasoning, Planning |
+
+Each row's `task_file` in `raw_metadata` names the source file it came from, one per task category. The numbers in the file name match the task codes in the original dataset card, for example `train_json_1_1_1.json` holds task 1.1.1.
+
+| View | Rows |
+|---|---|
+| GROUND | 196,293 |
+| SATELLITE | 69,713 |
+| UAV | 22,825 |
+
+255,396 rows have one image, 14,870 have two, 12,050 have four and 6,515 have five.
+
+## Structure
+
+```
+train-0000-of-0001.parquet
+images/
+  image-train-000-of-008.zip ... image-train-007-of-008.zip
+  path_to_shard.parquet
+```
+
+Each row has:
+
+- `images`: list of `{"bytes": null, "path": <sha256>}`, in the order the images appear in the question. The path is the SHA256 of the image bytes and points into a zip shard through `images/path_to_shard.parquet`.
+- `id`: `agroomni_train_<counter>`, for example `agroomni_train_0`.
+- `messages`: one user turn (question with images) and one assistant turn (answer).
+- `raw_metadata`: JSON string with `view_type` (`GROUND`, `UAV` or `SATELLITE`), `task_file` and `raw_image_paths`.
+
+Example:
+
+```json
+{
+  "images": [
+    {"bytes": null, "path": "8df98fdbcb2483eaba2d5be169859db0f60c106b461f32fbaf8622440089bc9f"},
+    {"bytes": null, "path": "fe73274fdc1728d4607631dcc39744aff34679743ebf39a394a0b0b38aa878e1"}
+  ],
+  "id": "agroomni_train_0",
+  "messages": [
+    {"role": "user", "content": [
+      {"type": "text", "text": "image1: "},
+      {"type": "image", "text": null},
+      {"type": "text", "text": ", image2: "},
+      {"type": "image", "text": null},
+      {"type": "text", "text": "Compare the pictures of two time periods to determine whether there is a mutual transformation between farmland and non-farmland at the specified coordinate pixel (351, 444)? (The upper left corner is (0,0); x is horizontal, y is vertical; The image is 512x512 in size.)"}
+    ]},
+    {"role": "assistant", "content": [{"type": "text", "text": "Yes"}]}
+  ],
+  "raw_metadata": "{\"view_type\": \"SATELLITE\", \"task_file\": \"train_json_3_4_2.json\", \"raw_image_paths\": [\"./CropLand/time1/00000.png\", \"./CropLand/time2/00000.png\"]}"
+}
+```
+
+### Changes from the original data
+
+- Built from `train.json`. All 288,831 rows are kept, matching the count in the paper, including 3 exact duplicate rows in the source.
+- `task_file` comes from the `train_json/` folder, which holds the same rows split into one file per task.
+- Turns are converted from `from`/`value` to `role`/`content`: `human` becomes `user` and `gpt` becomes `assistant`. All other text is kept as in the source.
+- Each `<image>` token in the question becomes an image item at the same place. Single-image questions start with `<image>` and a line break, so the image comes first and that line break is dropped. In multi-image questions the images stay between the text, so labels such as `A. ` or `image1: ` stay next to their image.
+- `raw_metadata` keeps `view_type` from the source. The image paths as written are kept in `raw_image_paths`.
+- All 117,158 source image files are packed, including 9,670 that no row uses, such as tree masks, extra tomato images, sample images and cropland labels. Images are stored once, deduplicated by SHA256, which leaves 115,035 unique images. The rows use 106,548 of them.
+
+## Usage
+
+```python
+from agml import loadImageTextToTextDataset
+
+dataset = loadImageTextToTextDataset("Project-AgML/AgroOmni")
+
+sample = dataset["train"][0]
+sample["images"]    # list of PIL images
+sample["messages"]
+```
+
+## Citation
+
+```bibtex
+@misc{zhang2026agroomni,
+      title={AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning},
+      author={Jiarui Zhang and Junqi Hu and Zurong Mai and Yang Liu and Yuhang Chen and Shuohong Lou and Henglian Huang and Hong Cheng and Lingyuan Zhao and Jianxi Huang and Yutong Lu and Haohuan Fu and Juepeng Zheng},
+      year={2026},
+      eprint={2603.14342},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2603.14342},
+}
+```
+
+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.

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