MUFAC (Machine Unlearning for Facial Age Classifier)

This repository provides a cleaned and resolution-aligned (128x128) version of the MUFAC benchmark dataset.

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📘 Description

A multi-class age classification dataset featuring over 63,000 Asian facial images with annotations for age groups and personal identities.

• 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).

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🗂️ Dataset Structure

MUFAC/
├── forget_images/                     # 1,431 images to be unlearned
├── retain_images/                     # 7,680 images to retain
├── train_images_part1/                # 9,093 training images (part 1)
├── train_images_part2/                # 18 training images (part 2)
├── val_images/                        # 1,464 validation images
├── test_images/                       # 1,408 test images
├── fixed_test_dataset_negative/       # 9,319 identity-balanced test data (negative)
├── fixed_test_dataset_positive/       # 9,319 identity-balanced test data (positive)
├── fixed_val_dataset_negative/        # 9,462 identity-balanced val data (negative)
├── fixed_val_dataset_positive/        # 9,424 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.

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🔹 How to Use

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()

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📊 Citation

This dataset is part of the benchmark suite introduced in the following paper:

@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

• 📧 Contact: [email protected]