Add files using upload-large-folder tool

README.md

@@ -25,7 +25,7 @@ The dataset contains annotations for 5 categories of document layout elements:
 
 Each sample contains:
 - `id`: Unique document identifier
-- `image`: Document image (PIL Image)
+- `image`: Document image (PIL Image) - automatically loaded from embedded bytes
 - `annotations`: List of layout element annotations with:
   - `category_id`: Element type (1-5)
   - `bbox`: Bounding box coordinates [x, y, width, height]
@@ -35,6 +35,13 @@ Each sample contains:
   - `image_id`: Reference to the document image
   - `segmentation`: Polygon segmentation mask
 
+## Data Storage
+
+Images are stored as embedded bytes in the parquet file and automatically converted to PIL Images when loaded. This ensures:
+- Self-contained dataset (no external image dependencies)
+- Fast loading and processing
+- Compatibility with HuggingFace datasets library
+
 ## Category Distribution
 
 This subset maintains diverse representation across categories:
@@ -57,8 +64,9 @@ for sample in dataset['train']:
     print(f"Document ID: {sample['id']}")
     print(f"Number of layout elements: {len(sample['annotations'])}")
     
-    # Access the image
-    image = sample['image']  # PIL Image
+    # Access the image (automatically converted to PIL Image)
+    image = sample['image']  # PIL Image object
+    print(f"Image size: {image.size}")
     
     # Access annotations
     for ann in sample['annotations']:
@@ -75,11 +83,22 @@ You can also load the data directly from the parquet file:
 ```python
 import pyarrow.parquet as pq
 import pandas as pd
+from PIL import Image as PILImage
+import io
 
 # Read parquet file
 table = pq.read_table("publaynet_mini.parquet")
 df = table.to_pandas()
 
+# Convert images from bytes to PIL Images
+def convert_image(img_data):
+    if isinstance(img_data, dict) and 'bytes' in img_data:
+        img_bytes = img_data['bytes']
+        return PILImage.open(io.BytesIO(img_bytes))
+    return img_data
+
+df['image'] = df['image'].apply(convert_image)
+
 # Access data
 for idx, row in df.iterrows():
     image = row['image']  # PIL Image

publaynet_mini.py

@@ -5,6 +5,8 @@ from datasets import Dataset, DatasetDict, Features, Image, Value, Sequence
 import pyarrow.parquet as pq
 import pandas as pd
 import numpy as np
+from PIL import Image as PILImage
+import io
 
 def _load_publaynet_mini():
     """Load PubLayNet_mini dataset from parquet file."""
@@ -27,6 +29,15 @@ def _load_publaynet_mini():
     
     df['annotations'] = df['annotations'].apply(convert_annotations)
     
+    # Convert image bytes to PIL Images
+    def convert_image(img_data):
+        if isinstance(img_data, dict) and 'bytes' in img_data:
+            img_bytes = img_data['bytes']
+            return PILImage.open(io.BytesIO(img_bytes))
+        return img_data
+    
+    df['image'] = df['image'].apply(convert_image)
+    
     # Define dataset features
     features = Features({
         "id": Value("string"),