Improve dataset card for CausalVerse_Image: Add paper, code, project page, tasks, license, and detailed usage (#2)

- Improve dataset card for CausalVerse_Image: Add paper, code, project page, tasks, license, and detailed usage (9699f25cd6a01ae9a8b9b0f326625bc56919dce0)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -53,6 +53,20 @@ dataset_info:
     num_bytes: 15431950241.0
   download_size: 136135745843.0
   dataset_size: 136135745843.0
+license: apache-2.0
+task_categories:
+- image-feature-extraction
+- object-detection
+- video-classification
+language:
+- en
+tags:
+- causal-representation-learning
+- simulation
+- robotics
+- traffic
+- physics
+- synthetic
 ---
 
 # CausalVerse Image Dataset
@@ -67,6 +81,36 @@ All splits share the same columns:
 - `render_path` (string; original image filename/path)
 - `metavalue` (string; per-sample metadata; schema varies by split)
 
+**Paper:** [CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations](https://huggingface.co/papers/2510.14049)
+**Project page:** [https://causal-verse.github.io/](https://causal-verse.github.io/)
+**Code:** [https://github.com/CausalVerse/CausalVerseBenchmark](https://github.com/CausalVerse/CausalVerseBenchmark)
+
+## Overview
+
+<p align="center"> <img src="https://github.com/CausalVerse/CausalVerseBenchmark/blob/main/assets/causalverse_intro.png?raw=true" alt="CausalVerse Overview Figure" width="85%">
+
+**CausalVerse** is a comprehensive benchmark for **Causal Representation Learning (CRL)** focused on *recovering the data-generating process*. It couples **high-fidelity, controllable simulations** with **accessible and configurable ground-truth causal mechanisms** (structure, variables, interventions, temporal dependencies), bridging the gap between **realism** and **evaluation rigor**.
+
+The benchmark spans **24 sub-scenes** across **four domains**:
+- 🖼️ Static image generation  
+- 🧪 Dynamic physical simulation  
+- 🤖 Robotic manipulation  
+- 🚦 Traffic scene analysis  
+
+Scenarios range from **static to temporal**, **single to multi-agent**, and **simple to complex** structures, enabling principled stress-tests of CRL assumptions. We also include reproducible baselines to help practitioners align **assumptions ↔ data ↔ methods** and deploy CRL effectively.
+
+## Dataset at a Glance
+
+<p align="center">
+  <img src="https://github.com/CausalVerse/CausalVerseBenchmark/blob/main/assets/causalverse_overall.png?raw=true" alt="CausalVerse Overview Figure" width="45%">
+  <img src="https://github.com/CausalVerse/CausalVerseBenchmark/blob/main/assets/causalverse_pie.png?raw=true" alt="CausalVerse data info Figure" width="49.4%">
+</p>
+
+- **Scale & Coverage**: ≈ **200k** high-res images, ≈ **140k** videos, **>300M** frames across **24 scenes** in **4 domains**  
+  - Image generation (4), Physical simulation (10; aggregated & dynamic), Robotic manipulation (5), Traffic (5)
+- **Resolution & Duration**: typical **1024×1024** / **1920×1080**; clips **3–32 s**; diverse frame rates
+- **Causal Variables**: **3–100+** per scene, including **categorical** (e.g., object/material types) and **continuous** (e.g., velocity, mass, positions). Temporal scenes combine **global invariants** (e.g., mass) with **time-evolving variables** (e.g., pose, momentum).
+
 ## Sizes (from repository files)
 - `scene1`: 11,736 examples — ~19.94 GB  
 - `scene2`: 11,736 examples — ~17.01 GB  
@@ -81,7 +125,9 @@ All splits share the same columns:
 > - `metavalue` is **split-specific** (e.g., `fall` uses keys like `id,h1,r,u,h2,view`, while `scene*` have attributes like `domain,age,gender,...`).  
 > - If you only need a portion, consider slicing (e.g., `split="fall[:1000]"`) or streaming to reduce local footprint.
 
-## Loading examples
+## Sample Usage
+
+### Loading with `datasets` library
 
 ```python
 from datasets import load_dataset
@@ -91,4 +137,168 @@ ds_fall = load_dataset("CausalVerse/CausalVerse_Image", split="fall")
 
 # Scene split
 ds_s1 = load_dataset("CausalVerse/CausalVerse_Image", split="scene1")
+```
+
+### Using the Image Dataset (PyTorch-ready)
+
+We provide a **reference PyTorch dataset/loader** that works with exported splits.
+
+*   Core class: `dataset/dataset_multisplit.py` → `MultiSplitImageCSVDataset`
+*   Builder: `build_dataloader(...)`
+*   Minimal example: `dataset/quickstart.py`
+
+**Conventions**
+
+*   Each split folder contains `<SPLIT>.csv` + `.png` files
+*   CSV must include **`render_path`** (relative to the repository root or chosen data root)
+*   All remaining CSV columns are treated as **metadata** and packed into a float tensor `meta`
+
+**Quick example**
+
+```python
+from dataset.dataset_multisplit import build_dataloader
+# Optional torchvision transforms:
+# import torchvision.transforms as T
+# tfm = T.Compose([T.Resize((256, 256)), T.ToTensor()])
+
+loader, ds = build_dataloader(
+    root="/path/to/causalverse",
+    split="SCENE1",
+    batch_size=16,
+    shuffle=True,
+    num_workers=4,
+    pad_images=True,  # zero-pads within a batch if resolutions differ
+    # image_transform=tfm,
+    # check_files=True,
+)
+
+for images, meta in loader:
+    # images: FloatTensor [B, C, H, W] in [0, 1]
+    # meta  : FloatTensor [B, D] with ordered metadata (including 'view' if present)
+    ...
+```
+
+> **`view` column semantics**:
+> • Physical splits (e.g., FALL/REFRACTION/SLOPE/SPRING): **camera viewpoint**
+> • Human rendering splits (SCENE1–SCENE4): **indoor background type**
+
+## Installation
+
+```bash
+# 1) Clone
+git clone https://github.com/CausalVerse/CausalVerseBenchmark.git
+cd CausalVerseBenchmark
+
+# 2) Core environment
+python3 --version    # >= 3.9 recommended
+pip install -U torch datasets huggingface_hub pillow tqdm
+
+# 3) Optional: examples / loaders / transforms
+pip install torchvision scikit-learn rich
+```
+
+## Download & Convert (Image subset)
+
+Fetch the **image** portion from Hugging Face and export to a simple on-disk layout (PNG files + per-split CSVs).
+
+**Quick start (recommended)**
+
+```bash
+chmod +x dataset/run_export.sh
+./dataset/run_export.sh
+```
+
+This will:
+
+*   download parquet shards (skip if local),
+*   export images to `image/<SPLIT>/*.png`,
+*   write `<SPLIT>.csv` next to each split with metadata columns + a `render_path` column.
+
+**Output layout**
+
+```
+image/
+  FALL/
+    FALL.csv
+    000001.png
+    ...
+  SCENE1/
+    SCENE1.csv
+    char_001.png
+    ...
+```
+
+<details>
+<summary><b>Custom CLI usage</b></summary>
+
+```bash
+python dataset/export_causalverse_image.py \
+  --repo-id CausalVerse/CausalVerse_Image \
+  --hf-home ./.hf \
+  --raw-repo-dir ./CausalVerse_Image \
+  --image-root ./image \
+  --folder-case upper \
+  --no-overwrite \
+  --include-render-path-column \
+  --download-allow-patterns data/*.parquet \
+  --skip-download-if-local
+
+# Export specific splits (case-insensitive)
+python dataset/export_causalverse_image.py --splits FALL SCENE1
+```
+
+</details>
+
+## Evaluation (Image Part)
+
+We release four reproducible baselines (shared backbone & similar training loop for fair comparison):
+
+*   `CRL_SC` — Sufficient Change
+*   `CRL_SF` — Mechanism Sparsity
+*   `CRL_SP` — Multi-view
+*   `SUP` — Supervised upper bound
+
+**How to run**
+
+```bash
+# From repo root, run each baseline:
+cd evaluation/image_part/CRL_SC && python main.py
+cd ../CRL_SF && python main.py
+cd ../CRL_SP && python main.py
+cd ../SUP && python main.py
+
+# Example: pass data root via env or args
+# DATA_ROOT=/path/to/causalverse python main.py
+```
+
+**Full comparison (MCC / R²)**
+
+| Algorithm | Ball on the Slope<br><sub>MCC / R²</sub> | Cylinder Spring<br><sub>MCC / R²</sub> | Light Refraction<br><sub>MCC / R²</sub> | Avg<br><sub>MCC / R²</sub> |
+|---|---:|---:|---:|---:|
+| **Supervised** | 0.9878 / 0.9962 | 0.9970 / 0.9910 | 0.9900 / 0.9800 | **0.9916 / 0.9891** |
+| **Sufficient Change** | 0.4434 / 0.9630 | 0.6092 / 0.9344 | 0.6778 / 0.8420 | 0.5768 / 0.9131 |
+| **Mechanism Sparsity** | 0.2491 / 0.3242 | 0.3353 / 0.2340 | 0.1836 / 0.4067 | 0.2560 / 0.3216 |
+| **Multiview** | 0.4109 / 0.9658 | 0.4523 / 0.7841 | 0.3363 / 0.7841 | 0.3998 / 0.8447 |
+| **Contrastive Learning** | 0.2853 / 0.9604 | 0.6342 / 0.9920 | 0.3773 / 0.9677 | 0.4323 / 0.9734 |
+
+
+> Ablations can be reproduced by editing each method’s `main.py` or adding configs (e.g., split selection, loss weights, target subsets).
+
+## Acknowledgements
+
+We thank the open-source community and the simulation/rendering ecosystem. We also appreciate contributors who help improve CausalVerse through issues and pull requests.
+
+## Citation
+
+If CausalVerse helps your research, please cite:
 
+```bibtex
+@inproceedings{causalverse2025,
+  title     = {CausalVerse: Benchmarking Causal Representation Learning with Configurable High-Fidelity Simulations},
+  author    = {Guangyi Chen and Yunlong Deng and Peiyuan Zhu and Yan Li and Yifan Shen and Zijian Li and Kun Zhang},
+  booktitle = {NeurIPS},
+  year      = {2025},
+  note      = {Spotlight},
+  url       = {https://huggingface.co/CausalVerse}
+}
+```