Update README.md

README.md

@@ -46,39 +46,32 @@ SSI-Bench is constructed from **complex real-world 3D structures**, where feasib
 ## Load Dataset
 ```
 from datasets import load_dataset
-
-dataset = load_dataset("cyang203912/SSI-Bench")
-print(dataset)
-```
-
-After downloading the parquet file, read each record, decode images from binary, and save them as JPG files.
-```
-import pandas as pd
 import os
 
-df = pd.read_parquet("SSI_Bench.parquet")
+ds = load_dataset("cyang203912/SSI-Bench")["test"]
+print(ds)
 
 output_dir = "./images"
 os.makedirs(output_dir, exist_ok=True)
 
-for _, row in df.iterrows():
-    index_val = row["index"]
-    images = row["image"]
-    question = row["question"]
-    answer = row["answer"]
-    annotation_color = row["annotation_color"]
-    category = row["category"]
-    task = row["task"]
+for ex in ds:
+    index_val = ex["index"]
+    images = ex["image"]     
+    question = ex["question"]
+    answer = ex["answer"]
+    annotation_color = ex["annotation_color"]
+    category = ex["category"]
+    task = ex["task"]
 
     image_paths = []
     if images is not None:
-        for n, img_data in enumerate(images):
-            image_path = f"{output_dir}/{index_val}_{n}.jpg"
-            with open(image_path, "wb") as f:
-                f.write(img_data)
+        if not isinstance(images, list):
+            images = [images]
+
+        for n, img in enumerate(images):
+            image_path = os.path.join(output_dir, f"{index_val}_{n}.jpg")
+            img.save(image_path)
             image_paths.append(image_path)
-    else:
-        image_paths = []
 
     print(f"index: {index_val}")
     print(f"image: {image_paths}")