Update README.md

README.md

@@ -18,8 +18,101 @@ annotations_creators:
 source_datasets:
 - danbooru
 ---
+# 🎨 Danbooru2024 Dataset
 
-# Danbooru2024 Dataset
+![Dataset Size](https://img.shields.io/badge/Images-6.5M-blue)
+![Language](https://img.shields.io/badge/Languages-EN%20|%20JA-green)
+![Category](https://img.shields.io/badge/Category-Image%20Classification-orange)
 
-6457843 original images in total.
+## 📊 Dataset Overview
 
+The **Danbooru2024** dataset is a comprehensive collection focused on animation and illustration artwork, derived from the official Danbooru platform. It contains approximately **6.5 million high-quality, user-annotated images** with corresponding tags and textual descriptions.
+
+This dataset is filtered from an original set of **8.3 million entries**, excluding NSFW-rated, **opt-out** entries to create a more accessible and audience-friendly resource. It addresses the challenges associated with overly crawled booru databases by providing a curated and well-structured solution.
+
+## ✨ Features
+
+### 📋 Metadata Support
+Includes a Parquet format metadata.
+
+Example code for usage:
+```python
+# install necessary packages, you can choose pyarrow or fastparquet
+#%pip install pandas pyarrow
+
+from tqdm.auto import tqdm
+import pandas as pd
+tqdm.pandas() # register progress_apply
+
+# read parquet file
+df = pd.read_parquet('metadata.parquet')
+print(df.head()) # check the first 5 rows
+
+#print(df.columns) # you can check the columns of the dataframe
+necessary_rows = [
+    "created_at", "score", "rating", "tag_string", "up_score", 
+    "down_score", "fav_count"
+]
+df = df[necessary_rows] # shrink the dataframe to only necessary columns
+df['created_at'] = pd.to_datetime(df['created_at']) # convert to datetime
+
+datetime_start = pd.Timestamp('2007-01-01', tz='UTC')
+datetime_end = pd.Timestamp('2008-01-01', tz='UTC')
+subdf = df[(df['created_at'] >= datetime_start) & 
+           (df['created_at'] < datetime_end)]
+
+# count some rating
+print(subdf['rating'].value_counts())
+# export subdataframe
+subdf.to_parquet('metadata-2007.parquet')
+```
+
+### 📥 Partial Downloads
+To simplify downloading specific entries, use the **[CheeseChaser](https://github.com/deepghs/cheesechaser)** library:
+
+```python
+from cheesechaser.datapool import DanbooruNewestDataPool
+from cheesechaser.query import DanbooruIdQuery
+
+pool = DanbooruNewestDataPool()
+#my_waifu_ids = DanbooruIdQuery(['surtr_(arknights)', 'solo']) 
+# above is only available when Danbooru is accessible, if not, use following:
+import pandas as pd
+
+# read parquet file
+df = pd.read_parquet('metadata.parquet', 
+                     columns=['id', 'tag_string']) # read only necessary columns
+                     
+#surtr_(arknights) -> gets interpreted as regex so we need to escape the brackets
+subdf = df[df['tag_string'].str.contains('surtr_\\(arknights\\)') & 
+           df['tag_string'].str.contains('solo')]
+ids = subdf.index.tolist()
+print(ids[:5]) # check the first 5 ids
+
+# download danbooru images with surtr+solo, to directory /data/exp2_surtr
+pool.batch_download_to_directory(
+    resource_ids=ids,
+    dst_dir='/data/exp2_surtr',
+    max_workers=12,
+)
+```
+
+## 🏷️ Dataset Information
+
+- **License**: Other
+- **Task Categories**:
+  - Image Classification
+  - Zero-shot Image Classification
+  - Text-to-Image
+- **Languages**:
+  - English
+  - Japanese
+- **Tags**:
+  - Art
+  - Anime
+- **Size Category**: 1M < n < 10M
+- **Annotation Creators**: No annotation
+- **Source Datasets**: [Danbooru](danbooru.donmai.us)
+
+---
+*Note: This dataset is provided for research and development purposes. Please ensure compliance with all applicable usage terms and conditions.*