Update README.md

README.md

@@ -66,17 +66,38 @@ The benchmark assesses models using a specialized level-aware scoring mechanism
 
 ## 🚀 How to Use
 
-You can easily load and evaluate on the MMTR-Bench dataset using the `datasets` library from Hugging Face.
+MMTR-Bench is an evaluation-only benchmark designed to test Multimodal LLMs. There is no training set. 
+
+The dataset annotations (including mask bounding boxes, ground truth answers, and image paths) are stored in the metadata file, and the images are located in the `images/` directory.
+
+### 1. Installation
+Ensure you have the required libraries installed:
+```bash
+pip install datasets
+```
+
+### 2. Loading the Benchmark
+*(Note: If this repository is currently private for double-blind review, make sure to authenticate using your Hugging Face token.)*
 
 ```python
 from datasets import load_dataset
 
-# Load the dataset
-# Please replace 'Your-Username' with your actual Hugging Face username
-dataset = load_dataset("Your-Username/MMTR_Bench_Dateset")
-
-# Inspect the first test sample
-print(dataset['test'][0])
+# Load the benchmark dataset
+# Note: Hugging Face maps single data files to the 'train' split by default.
+dataset = load_dataset(
+    "MMTR-Bench/MMTR_Bench_Dateset", 
+    data_files="metadata.json" # or metadata.jsonl
+)
+benchmark_data = dataset["train"]
+
+# Inspect the first evaluation sample
+sample = benchmark_data[0]
+
+print(f"Sample ID: {sample['sample_id']}")
+print(f"Difficulty Level: L{sample['level']}")
+print(f"Ground Truth Answer: {sample['answer']}")
+print(f"Mask Bounding Box: {sample['bbox']}")
+print(f"Target Image: {sample['image_path']}")
 ```
 
 ---