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README.md

@@ -42,11 +42,11 @@ This repo contains evaluation code for the paper "[MMSI-Bench: A Benchmark for M
 
 
 ## 🔔News
-  <!-- **🔥[2025-05-31]: MMSI-Bench has been supported in the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) repository.** -->
+  **🔥[2025-06-9]: MMSI-Bench has been supported in the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) repository.**
 
- **🔥[2025-05-30]: We released the ArXiv paper.**
+  **🔥[2025-05-30]: We released the ArXiv paper.**
 
- ## Load Dataset
+## Load Dataset
 ```
 from datasets import load_dataset
 
@@ -54,6 +54,42 @@ mmsi_bench = load_dataset("RunsenXu/MMSI-Bench")
 print(mmsi_bench)
 ```
 
+## 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('MMSI_Bench.parquet')
+
+output_dir = './images'
+os.makedirs(output_dir, exist_ok=True)
+
+for idx, row in df.iterrows():
+    id_val = row['id']
+    images = row['images']  
+    question_type = row['question_type']
+    question = row['question']
+    answer = row['answer']
+    thought = row['thought']
+
+    image_paths = []
+    if images is not None:
+        for n, img_data in enumerate(images):
+            image_path = f"{output_dir}/{id_val}_{n}.jpg"
+            with open(image_path, "wb") as f:
+                f.write(img_data)
+            image_paths.append(image_path)
+    else:
+        image_paths = []
+
+    print(f"id: {id_val}")
+    print(f"images: {image_paths}")
+    print(f"question_type: {question_type}")
+    print(f"question: {question}")
+    print(f"answer: {answer}")
+    print(f"thought: {thought}")
+    print("-" * 50)
+```
 
 ## Evaluation
 Please refer to the [evaluation guidelines](https://github.com/open-compass/VLMEvalKit/blob/main/docs/en/Quickstart.md) of [VLMEvalKit](https://github.com/open-compass/VLMEvalKit)