Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: mit
+task_categories:
+- image-classification
+language:
+- en
+---
+
+# MUFAC (Machine Unlearning for Facial Age Classifier)
+
+[![arXiv](https://img.shields.io/badge/arXiv-2311.02240-b31b1b.svg)](https://arxiv.org/abs/2311.02240)
+
+This repository provides a **cleaned and resolution-aligned (128x128) version** of the MUFAC benchmark dataset.
+
+---
+
+## 📘 Description
+
+A multi-class age classification dataset featuring over 86,000 Asian facial images with annotations for age groups and personal identities.
+
+<img src="https://raw.githubusercontent.com/ndb796/MachineUnlearning/main/resources/MUFAC.png" width="700" alt="MUFAC examples"/>
+
+- Preprocessed facial images (128×128 resolution)
+- CSV files (`custom_train_dataset.csv`, etc.) for structured training/evaluation
+- Separation of identity-forgettable vs. retained images (`forget_images`, `retain_images`)
+- Suitable for benchmarking **machine unlearning** algorithms, especially in **task-agnostic setups**
+
+It is specifically intended for experiments where **personal identities are selectively unlearned**, without degrading model utility on the original task (e.g., age classification).
+
+---
+
+## 🗂️ Dataset Structure
+```
+MUFAC/
+├── forget_images/                     # 1,500 images to be unlearned
+├── retain_images/                     # 8,525 images to retain
+├── train_images_part1/                # 9,999 training images (part 1)
+├── train_images_part2/                # 26 training images (part 2)
+├── val_images/                        # 1,539 validation images
+├── test_images/                       # 4,513 test images
+├── fixed_test_dataset_negative/       # 5,000 identity-balanced test data (negative)
+├── fixed_test_dataset_positive/       # 5,000 identity-balanced test data (positive)
+├── fixed_val_dataset_negative/        # 5,000 identity-balanced val data (negative)
+├── fixed_val_dataset_positive/        # 5,000 identity-balanced val data (positive)
+├── custom_train_dataset.csv           # CSV with image paths and labels
+├── custom_val_dataset.csv             # Validation CSV
+└── custom_test_dataset.csv            # Test CSV
+```
+- CSV files follow the format: `image_path`, `age_group`, `identity`, `forget_flag`, etc.
+- All image paths are relative and usable with `datasets.Image()` or `PIL`.
+
+---
+## 🔹 How to Use
+```python
+# Method 1: Git Clone (Recommended)
+git lfs install
+git clone https://huggingface.co/datasets/Dasool/MUFAC
+cd MUFAC
+```
+
+# Method 2: Using Hugging Face Hub API
+```
+from huggingface_hub import snapshot_download
+
+# Download entire dataset
+local_dir = snapshot_download("Dasool/MUFAC", repo_type="dataset")
+print(f"Dataset downloaded to: {local_dir}")
+```
+# Method 3: Load CSV and Images
+```
+import pandas as pd
+import os
+from PIL import Image
+
+# Load CSV
+df = pd.read_csv("MUFAC/custom_train_dataset.csv")
+print(f"Dataset size: {len(df)} samples")
+print(df.head())
+
+# Load sample image
+sample_row = df.iloc[0]
+img_path = os.path.join("MUFAC", sample_row["image_path"])
+img = Image.open(img_path)
+img.show()
+```
+
+---
+## 📊 Citation
+
+This dataset is part of the benchmark suite introduced in the following paper:
+
+```bibtex
+@misc{choi2023machine,
+  title={Towards Machine Unlearning Benchmarks: Forgetting the Personal Identities in Facial Recognition Systems},
+  author={Dasol Choi and Dongbin Na},
+  year={2023},
+  eprint={2311.02240},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+```
+
+- 🔗 Code for Experiments:: [https://github.com/ndb796/MachineUnlearning](https://github.com/ndb796/MachineUnlearning)
+
+- 📧 Contact: [email protected]