Update README.md

README.md

@@ -9,7 +9,8 @@ tags:
 - land-use
 - remote-sensing
 - vqa
-pretty_name: MM-LUCAS
+- vision-language
+pretty_name: AgriBench
 configs:
 - config_name: train
   data_files:
@@ -20,11 +21,13 @@ dataset_info:
   splits:
   - name: train
     num_examples: 7136
+size_categories:
+- 1K<n<10K
 ---
 
-# MM-LUCAS
+# AgriBench
 
-MM-LUCAS is a multimodal benchmark built on top of the EU's LUCAS (Land Use/Cover Area frame Survey) land-cover survey, pairing 1,784 landscape photos (1600×1200, taken across 27 EU countries) with microdata the survey already recorded: GPS location, country, capture date, land-cover and land-use taxonomy codes, and quality/aesthetic ratings. It's the dataset behind AgriBench, the first benchmark built to evaluate multimodal LLMs on agriculture tasks.
+AgriBench is the first benchmark built to evaluate multimodal LLMs on agriculture tasks. This repo is its MM-LUCAS subset: built on top of the EU's LUCAS (Land Use/Cover Area frame Survey) land-cover survey, it pairs 1,784 landscape photos (1600×1200, taken across 27 EU countries) with microdata the survey already recorded — GPS location, country, capture date, land-cover and land-use taxonomy codes, and quality/aesthetic ratings.
 
 Paper: [AgriBench: A Hierarchical Agriculture Benchmark for Multimodal Large Language Models](https://doi.org/10.1007/978-3-031-91835-3_14) (arXiv:2412.00465, ECCV 2024 Workshops)
 Authors: Yutong Zhou, Masahiro Ryo
@@ -80,7 +83,7 @@ Images are content-addressed by SHA256 hash and stored uncompressed in zip shard
 | Column | Type | Description |
 |---|---|---|
 | `images` | `list<struct<bytes, path>>` | `bytes` is `null`; `path` is the SHA256 hash, looked up in `images/path_to_shard.parquet` |
-| `id` | `string` | `mmlucas_<task>_<n>` |
+| `id` | `string` | `mmlucas_<task>_<n>` (names the MM-LUCAS subset this repo standardizes) |
 | `messages` | chat-style list | user turn: image + question (multiple-choice questions include lettered options inline); assistant turn: the answer, letter-prefixed for multiple-choice questions |
 | `raw_metadata` | JSON string | `task`, `image_path` (original source path), `gps_long`, `gps_lat`, `date`, `nuts0` (EU country code), `Classes` (free-text scene description); plus the task-specific field: `Aesthetic Score`, `Quality Score`, `lc1`/`lc1_label`, or `lu1`/`lu1_label` |
 
@@ -93,7 +96,7 @@ Recommended, via the AgML python library:
 ```python
 from agml import loadImageTextToTextDataset
 
-ds = loadImageTextToTextDataset("MM-LUCAS")
+ds = loadImageTextToTextDataset("AgriBench")
 ```
 
 ## Citations
@@ -130,4 +133,4 @@ ds = loadImageTextToTextDataset("MM-LUCAS")
 
 ---
 
-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 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.