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diff --git a/packing_images.py b/packing_images.py
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index 0000000000000000000000000000000000000000..de77f1d180d98238af32aa3d8f023a981bb0ef8a
--- /dev/null
+++ b/packing_images.py
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+
+import os
+import glob
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+from tqdm import tqdm
+import cv2 as cv
+import argparse
+
+
+def images_to_parquet_sharded(image_dir, output_path, output_prefix="data_shard", shard_size_mb=100):
+    """
+    Converts a folder of images into multiple Parquet shard files (~shard_size_mb each).
+
+    Args:
+        image_dir (str): Path to dataset folder. If images are grouped in
+                         subfolders, the folder name will be used as label.
+        output_path (str): Path to processed dataset folder. 
+        output_prefix (str): Prefix for output parquet files (e.g., "data_shard").
+        shard_size_mb (int): Approximate shard size in MB.
+    """
+    shard_size_bytes = shard_size_mb * 1024 * 1024
+    shard_index = 0
+    records = []
+    all_images_count = 0
+    total_written = 0
+
+    image_paths = glob.glob(os.path.join(image_dir, "**", "*.*"), recursive=True)
+
+    for path in tqdm(image_paths, desc="Packing images"):
+        ext = os.path.splitext(path)[1].lower()
+        if ext not in [".jpg", ".jpeg", ".png", ".bmp", ".gif", ".webp"]:
+            continue
+
+        label = os.path.basename(os.path.dirname(path))
+
+        img_bytes = cv.imread(path)
+        img_bytes = cv.imencode('.png', img_bytes)[1].tobytes()
+
+        records.append({"image": img_bytes, "label": label, "filename": os.path.basename(path)})
+        total_written += len(img_bytes)
+
+        # When current shard size exceeds limit, flush to disk
+        if total_written >= shard_size_bytes:
+            df = pd.DataFrame(records)
+            table = pa.Table.from_pandas(df)
+            out_path = os.path.join(output_path, f"{output_prefix}-{shard_index:05d}.parquet")
+            pq.write_table(table, out_path)
+            all_images_count += len(records)
+            # Reset for next shard
+            shard_index += 1
+            records = []
+            total_written = 0
+            
+
+    # Write leftover records
+    if records:
+        df = pd.DataFrame(records)
+        table = pa.Table.from_pandas(df)
+        out_path = os.path.join(output_path, f"{output_prefix}-{shard_index:05d}.parquet")
+        pq.write_table(table, out_path)
+        all_images_count += len(records)
+        
+    print(f"✅ Wrote {all_images_count} images to {output_path}")
+
+
+
+if __name__ == "__main__":
+    # Create an ArgumentParser object
+    parser = argparse.ArgumentParser(
+        description="This script takes an images folder and convert them into parquet files."
+    )
+
+    # Add arguments
+    parser.add_argument(
+        "--output",
+        "-o",
+        type=str,
+        required=True,
+        help="Output path for the resulting dataset.",
+    )
+    parser.add_argument(
+        "--output_prefix",
+        "-op",
+        type=str,
+        required=False,
+        default="data_shard",
+        help="Output prefix for the resulting dataset.",
+    )
+    parser.add_argument(
+        "--input",
+        "-i",
+        type=str,
+        required=True,
+        help="Input path for the dataset to convert.",
+    )
+    parser.add_argument(
+        "--parquet_shard_size_mb",
+        "-ps",
+        type=int,
+        default=100,
+        required=False,
+        help="Size of the parquet shard in MB.",
+    )
+
+    args = parser.parse_args()
+
+    # Example usage
+    images_to_parquet_sharded(args.input, args.output, output_prefix=args.output_prefix, shard_size_mb=args.parquet_shard_size_mb)