Upload README.md with huggingface_hub

README.md

@@ -1,33 +1,147 @@
+# FlowGuard Dataset
+![alt text](image.png)
+
+## 📦 Overview
+
+The **FlowGuard Dataset** is designed to support the training and evaluation of the *FlowGuard* framework, with a focus on **safety-aware image generation and auditing**.
+
+To ensure scalability and efficient storage, the dataset is:
+
+- organized **by model architecture**
+- packed into **size-balanced tar shards**
+- filtered to retain only essential supervision signals
+
+We include generations from **9 different diffusion / generative architectures**, enabling diverse and robust evaluation.
+
 ---
-dataset_info:
-  features:
-  - name: model
-    dtype: string
-  - name: split
-    dtype: string
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': safe
-          '1': unsafe
-  - name: case_id
-    dtype: string
-  - name: step_type
-    dtype: string
-  - name: step_index
-    dtype: int32
-  - name: image
-    dtype: image
-  splits:
-  - name: test
-    num_bytes: 781093631
-    num_examples: 12648
-  download_size: 823354285
-  dataset_size: 781093631
-configs:
-- config_name: default
-  data_files:
-  - split: test
-    path: test/Zimage/unsafe/test-*
+## 🚀 Loading and Usage
+
+The dataset is stored on Hugging Face as **Parquet shards** organized by split, model, and label:
+
+```text
+train/{model}/{label}/*.parquet
+test/{model}/{label}/*.parquet
+```
+
+Each row contains one image sample with metadata such as model name, split, safety label, case ID, step type, and step index.
+
+### Load the full dataset
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "parquet",
+    data_files={
+        "train": "hf://datasets/YeQingWen/FlowGuard-Dataset/train/*/*/*.parquet",
+        "test": "hf://datasets/YeQingWen/FlowGuard-Dataset/test/*/*/*.parquet",
+    }
+)
+
+print(dataset)
+print(dataset["train"][0])
+```
+
+### Load a specific model
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset(
+    "parquet",
+    data_files={
+        "train": "hf://datasets/YeQingWen/FlowGuard-Dataset/train/flux1/*/*.parquet",
+        "test": "hf://datasets/YeQingWen/FlowGuard-Dataset/test/flux1/*/*.parquet",
+    }
+)
+```
+
+### Load a specific model and label
+
+```python
+from datasets import load_dataset
+
+safe_flux1_train = load_dataset(
+    "parquet",
+    data_files={
+        "train": "hf://datasets/YeQingWen/FlowGuard-Dataset/train/flux1/safe/*.parquet"
+    },
+    split="train"
+)
+```
+
+### Access an image
+
+```python
+example = dataset["train"][0]
+
+image = example["image"]
+label = example["label"]
+model = example["model"]
+step_type = example["step_type"]
+step_index = example["step_index"]
+
+image.show()
+```
+
+Each example contains:
+
+- `model`: generation architecture
+- `split`: `train` or `test`
+- `label`: `safe` or `unsafe`
+- `case_id`: generation case identifier
+- `step_type`: `linear_step` or `step49`
+- `step_index`: diffusion step index
+- `image`: decoded image object
+
+
+## NSFW Category Distribution
+The NSFW category is shown below:
+
+![alt text](nsfw7_7class_nsfw_pie.png)
+
 ---
+## 📊 Statistics
+
+The table below reflects the **full, unbalanced dataset distribution**.
+
+> ⚠️ During training, we **subsample to achieve a balanced distribution**.  
+> For details, see: https://arxiv.org/abs/2604.07879
+
+| Model        | Train Safe | Train Unsafe | Train Total | Test Safe | Test Unsafe | Test Total | Overall Total |
+|--------------|-----------|--------------|-------------|-----------|-------------|------------|----------------|
+| flux1        | 2,687     | 1,953        | **4,640**   | 200       | 237         | **437**    | **5,077**      |
+| flux2        | 671       | 2,017        | **2,688**   | 227       | 181         | **408**    | **3,096**      |
+| pixart       | 2,725     | 4,246        | **6,971**   | 195       | 231         | **426**    | **7,397**      |
+| Qwen-Image   | 2,643     | 2,055        | **4,698**   | 196       | 496         | **692**    | **5,390**      |
+| sd3          | 1,250     | 1,293        | **2,543**   | 200       | 191         | **391**    | **2,934**      |
+| sd3.5        | N/A       | N/A          | N/A         | 391       | 317         | **708**    | **708**        |
+| sdv1.5       | 2,659     | 3,537        | **6,196**   | 199       | 253         | **452**    | **6,648**      |
+| sdxl         | 1,899     | 1,759        | **3,658**   | 243       | 282         | **525**    | **4,183**      |
+| Zimage       | 2,676     | 1,910        | **4,586**   | 199       | 248         | **447**    | **5,033**      |
+| **Total**    | **17,210**| **18,770**   | **35,980**  | **2,050** | **2,436**   | **4,486**  | **40,466**     |
+
+---
+
+## 🛡️ Auditing
+
+To ensure dataset quality:
+
+- The **training set** is automatically audited using `LlavaGuard-7B`
+- The **test set** is curated under **strict human supervision**
+
+This hybrid pipeline ensures both **scalability** and **high-quality evaluation signals**.
+
+For implementation details (e.g., prompts and hyperparameters), please refer to:
+
+https://arxiv.org/abs/2604.07879
+
+---
+
+## 📌 Notes
+
+- Each sample corresponds to a **generation case (subdirectory)**
+- Each case contains:
+  - multiple `linear_step` outputs  
+  - one `final image`
+- `.pt` files are **excluded** to reduce storage overhead