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README.md

@@ -1,9 +1,5 @@
 ---
 configs:
-- config_name: default
-  data_files:
-  - split: split_20250822_230610
-    path: data/split_20250822_230610-*
 - config_name: split_20250821_120925
   data_files:
   - split: train
@@ -144,6 +140,10 @@ configs:
   data_files:
   - split: train
     path: data/split_20250822_225145.parquet
+- config_name: split_20250822_230610
+  data_files:
+  - split: train
+    path: data/split_20250822_230610.parquet
 dataset_info:
   features:
   - name: media_hash
@@ -158,17 +158,54 @@ dataset_info:
     dtype: float32
   - name: media_image
     dtype: image
-  splits:
-  - name: train
-    num_bytes: 0
-    num_examples: 0
-  - name: split_20250822_230610
-    num_bytes: 93688.0
-    num_examples: 3
-  download_size: 95788
-  dataset_size: 93688.0
 ---
 
 # BitMind Image Benchmarks
 
-AI-generated image detection benchmark dataset.
+This dataset contains AI-generated image samples.
+
+## Dataset Structure
+
+Each config represents a batch upload with:
+- Parquet files in `data/` containing image data and metadata
+
+## Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# List available configs (timestamps)
+configs = ['split_20250821_110436', 'split_20250821_112432', ...]
+
+# Load specific config
+dataset = load_dataset('bitmind/bm-image-benchmarks', 'split_20250821_110436')
+
+# Access data
+for sample in dataset['train']:
+    print(f"Model: {sample['model_name']}")
+    print(f"Label: {sample['label']}")
+    # sample['media_image'] contains the PIL Image
+    image = sample['media_image']
+    image.show()  # Display the image
+```
+
+## Processing Images
+
+```python
+from datasets import load_dataset
+from PIL import Image
+
+# Load dataset (images and metadata)
+config = 'split_20250821_110436'  # Use your desired config
+dataset = load_dataset('bitmind/bm-image-benchmarks', config)
+
+# Process images with metadata
+for sample in dataset['train']:
+    image = sample['media_image']  # PIL Image object
+    print(f"Model: {sample['model_name']}")
+    print(f"Label: {sample['label']}")
+    print(f"Hash: {sample['media_hash']}")
+    
+    # Your image processing code here
+    # image.save(f"{sample['media_hash']}.png")
+```