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README.md

@@ -100,9 +100,20 @@ You can load the dataset directly using the Hugging Face 🤗 `datasets` library
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("RyanWW/Spatial457", name="L5_6d_spatial", split="validation", data_dir=".")
+# Load dataset from Hugging Face Hub
+dataset = load_dataset(
+    "RyanWW/Spatial457", 
+    name="L5_6d_spatial", 
+    split="validation",
+    trust_remote_code=True  # Required for custom loading script
+)
 ```
 
+**Important Notes:**
+- ✅ Use `trust_remote_code=True` to enable the custom dataset loading script
+- ❌ Do NOT use `data_dir` parameter when loading from Hugging Face Hub
+- 📦 Images are automatically downloaded and cached
+
 Each example is a dictionary like:
 
 ```python
@@ -116,17 +127,44 @@ Each example is a dictionary like:
 }
 ```
 
-### 🔹 Other available configurations
+### 🔹 Available Configurations
 
 ```python
-[
-  "L1_single", "L2_objects", "L3_2d_spatial",
-  "L4_occ", "L4_pose", "L5_6d_spatial", "L5_collision"
+configs = [
+  "L1_single",      # Single object identification
+  "L2_objects",     # Multi-object understanding
+  "L3_2d_spatial",  # 2D spatial reasoning
+  "L4_occ",         # Object occlusion
+  "L4_pose",        # 3D pose estimation
+  "L5_6d_spatial",  # 6D spatial reasoning
+  "L5_collision"    # Collision detection
 ]
 ```
 
 You can swap `name="..."` in `load_dataset(...)` to evaluate different spatial reasoning capabilities.
 
+### 🔹 Example: Load and Use
+
+```python
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset(
+    "RyanWW/Spatial457",
+    name="L5_6d_spatial",
+    split="validation",
+    trust_remote_code=True
+)
+
+print(f"Number of examples: {len(dataset)}")
+
+# Access first example
+example = dataset[0]
+print(f"Question: {example['question']}")
+print(f"Answer: {example['answer']}")
+print(f"Image size: {example['image'].size}")
+```
+
 ## 📊 Benchmark
 
 We benchmarked a wide range of state-of-the-art models—including GPT-4o, Gemini, Claude, and several open-source LMMs—across all subsets. The results below have been updated after rerunning the evaluation. While they show minor variance compared to the results in the published paper, the conclusions remain unchanged.