update readme

README.md

@@ -145,10 +145,10 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 3852500
+    num_bytes: 3239120
     num_examples: 10223
   download_size: 0
-  dataset_size: 3852500
+  dataset_size: 3239120
 - config_name: hypothetical_scenes_rendered_h5_nonlinear
   features:
   - name: image
@@ -336,6 +336,44 @@ dataset_info:
 
 **Causal3D** is a comprehensive benchmark designed to evaluate models’ abilities to uncover *latent causal relations* from structured and visual data. This dataset integrates **3D-rendered scenes** with **tabular causal annotations**, providing a unified testbed for advancing *causal discovery*, *causal representation learning*, and *causal reasoning* with **vision-language models (VLMs)** and **large language models (LLMs)**.
 
+## 📚 Usage
+
+#### 🔹 Option 1: Load from Hugging Face
+
+You can easily load a specific scene using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "LLDDSS/Causal3D",
+    name="real_scenes_Real_Parabola",
+    download_mode="force_redownload",  # Optional: force re-download
+    trust_remote_code=True             # Required for custom dataset loading
+)
+
+print(dataset)
+```
+
+#### 🔹 Option 2: Download via [**Kaggle**](https://www.kaggle.com/datasets/dsliu0011/causal3d-image-dataset) + Croissant
+```
+import mlcroissant as mlc
+import pandas as pd
+
+# Load the dataset metadata from Kaggle
+croissant_dataset = mlc.Dataset(
+    "https://www.kaggle.com/datasets/dsliu0011/causal3d-image-dataset/croissant/download"
+)
+
+# List available record sets
+record_sets = croissant_dataset.metadata.record_sets
+print(record_sets)
+
+# Load records from the first record set
+df = pd.DataFrame(croissant_dataset.records(record_set=record_sets[0].uuid))
+print(df.head())
+```
+
 ---
 
 ## 📌 Overview