update readme

README.md

@@ -52,6 +52,45 @@ Below are example images from different Causal3D scenes:
 - `split.json`: Recommended train/val/test splits for benchmarking. -->
 
 
+## 🗂️ Available Scenes
+
+Below is the full list of **builder configs** you can load using `load_dataset`.
+
+### 🔬 Hypothetical Scenes
+
+| Config Name | Description |
+| ------------------------------- | ------------------------------------------ |
+| `Hypothetical_V2_linear` | 2 variables, linear causal relationship |
+| `Hypothetical_V2_nonlinear` | 2 variables, non-linear causal relationship |
+| `Hypothetical_V3_fully_connected_linear` | 3 variables, fully connected, linear |
+| `Hypothetical_V3_v_structure_linear` | 3 variables, V-structure, linear |
+| `Hypothetical_V3_v_structure_nonlinear` | 3 variables, V-structure, non-linear |
+| `Hypothetical_V4_linear` | 4 variables, linear causal relationship |
+| `Hypothetical_V4_v_structure_nonlinear` | 4 variables, V-structure, non-linear |
+| `Hypothetical_V4_v_structure_linear` | 4 variables, V-structure, linear |
+| `Hypothetical_V5_linear` | 5 variables, linear causal relationship |
+| `Hypothetical_V5_v_structure_linear` | 5 variables, V-structure, linear |
+| `Hypothetical_V5_v_structure_nonlinear` | 5 variables, V-structure, non-linear |
+
+---
+
+### 🌍 Real-World Scenes
+
+| Config Name | Description |
+| ------------------------------- | ------------------------------------------ |
+| `Real_Parabola` | Real-world parabola trajectory |
+| `Real_Magnet` | Real-world magnetic force |
+| `Real_Spring` | Real-world spring oscillation |
+| `Real_Water_flow` | Real-world water flow dynamics |
+| `Real_Seesaw` | Real-world seesaw balance physics |
+| `Real_Reflection` | Real-world light reflection |
+| `Real_Pendulum` | Real-world pendulum motion |
+| `Real_Convex_len` | Real-world convex lens refraction |
+
+---
+
+
+
 ## 📚 Usage
 
 #### 🔹 Option 1: Load from Hugging Face
@@ -61,31 +100,13 @@ 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
+ds = load_dataset(
+    "LLDDSS/Causal3D_Dataset",
+    name="Real_Parabola",              # Replace with desired scene config name
     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
-```python
-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"
-)
-
-record_sets = croissant_dataset.metadata.record_sets
-print(record_sets)
-
-df = pd.DataFrame(croissant_dataset.records(record_set=record_sets[0].uuid))
-print(df.head())
+print(ds)
 ```
 
 ---
@@ -133,18 +154,3 @@ We evaluate a diverse set of methods:
 
 - As causal structures grow more complex, **model performance drops significantly** without strong prior assumptions.
 - A noticeable performance gap exists between models trained on structured data and those applied directly to visual inputs.
-
----
-
-
-
-<!-- ## 🔍 Example Use Case
-
-```python
-from causal3d import load_scene_data
-
-scene = "SpringPendulum"
-data = load_scene_data(scene, split="train")
-images = data["images"]
-metadata = data["table"]
-graph = data["causal_graph"] -->