Updated README.md with dataset details and `dataset_info` configs

README.md

@@ -1,16 +1,23 @@
 ---
-license: cc-by-nc-4.0
 configs:
 - config_name: default
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-nc-4.0
+task_categories:
+- image-text-to-text
+language:
+- en
+size_categories:
+- 100K<n<1M
 dataset_info:
+- config_name: default
   features:
   - name: images
     list:
       image:
-        decode: false
+        decode: true
   - name: id
     dtype: string
   - name: messages
@@ -29,8 +36,90 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 3091580570
     num_examples: 145261
-  download_size: 3070223641
-  dataset_size: 3091580570
 ---
+
+# CDDM (Crop Disease Domain Multimodal) Dataset
+
+CDDM is a large-scale multimodal benchmark dataset built to advance vision-language
+models for crop disease diagnosis. It pairs 137,000 crop disease images with over 1
+million instruction-following question-answer conversations covering disease
+identification, causes, symptoms, and prevention/treatment strategies.
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python
+library. Standardized to the HF `image_text_to_text` format with a single conversational
+`messages` schema, converted to **Parquet** with image bytes embedded directly.
+
+## Dataset Construction
+
+The image data was compiled from two sources:
+
+| Source | Images | Description |
+|---|---|---|
+| Web Data | 62,000 | Public agricultural datasets (Kaggle) plus web-crawled disease images |
+| Private Data | 75,000 | Original images collected via field surveys across multiple farms and orchards |
+
+All images were annotated by agricultural experts with crop category, disease category,
+and appearance description. The dataset spans **16 crop categories** and **60 crop disease categories**; 
+48 categories contain 500+ images each, with the remaining 7 containing 200–500 images.
+
+Two instruction-following data types were generated using GPT-4 prompting:
+- **Crop Disease Diagnosis QA** — over 1 million multi-turn QA pairs per image, covering
+  crop/disease identification, including deliberately-crafted negative-answer questions
+  to counter models' tendency toward false-positive diagnoses. Avg. question length: 6.11
+  words; avg. answer length: 8.92 words.
+- **Crop Disease Knowledge QA** — QA pairs generated from expert-curated disease
+  knowledge text (symptoms, pathogen characteristics, transmission, prevention/control).
+  Avg. question length: 9.69 words; avg. answer length: 130.41 words.
+
+A held-out test set of 3,000 images (not included in training data) was used by the
+original authors for benchmark evaluation.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Project-AgML/CDDM")
+first = ds["train"][0]
+
+# Access an image — decoded to PIL automatically
+img = first["images"][0]
+img.show()
+```
+
+## Schema
+
+Every record shares the SAME columns so heterogeneous AgML datasets concatenate cleanly:
+`id`, `images` (embedded image bytes), `messages`, `origin_dataset`, and `raw_metadata`.
+
+`raw_metadata` is a JSON-encoded string holding source fields not folded into `messages`
+(here: `file_names` pointing to the original image path, and annotated `crop_category` /
+`disease_category` where available); restore it with `json.loads(row["raw_metadata"])`.
+Image placeholders in `messages` align 1:1 with the `images` column.
+
+## Citation
+
+```bibtex
+@inproceedings{liu2024cddm,
+      title={A Multimodal Benchmark Dataset and Model for Crop Disease Diagnosis},
+      author={Liu, Xiang and Liu, Zhaoxiang and Hu, Huan and Chen, Zezhou and Wang, Kohou and Wang, Kai and Lian, Shiguo},
+      booktitle={Computer Vision -- ECCV 2024},
+      pages={157--170},
+      year={2025},
+      publisher={Springer Nature Switzerland},
+      address={Cham},
+      isbn={978-3-031-73016-0},
+      doi={10.1007/978-3-031-73016-0_10}
+}
+
+Liu, Xiang; Liu, Zhaoxiang; Hu, Huan; Chen, Zezhou; Wang, Kohou; Wang, Kai; Lian, Shiguo (2025), "A Multimodal Benchmark Dataset and Model for Crop Disease Diagnosis", ECCV 2024, pp. 157-170
+```
+
+## License
+
+Released by the original authors (China Unicom AI Innovation Center) as an open-source
+initiative for agricultural multimodal research. Original source and download instructions:
+https://github.com/UnicomAI/UnicomBenchmark/tree/main/CDDMBench. This license
+information is for reference only and does not constitute legal advice — refer to the
+original repository for the authoritative license terms.