FaceGuard-20ID-ViT

FaceGuard – ViT (20 CelebA IDs)

A Vision Transformer (ViT-Base) fine-tuned for identity classification on a 20-identity subset of the CelebA dataset.
This model predicts anonymized celeb_id integers (not celebrity names).
It powers the demo Space: https://huggingface.co/spaces/hudaakram/FaceGuard-demo

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Model Details

Model Description

• Architecture: google/vit-base-patch16-224 (pretrained on ImageNet-1k)
• Fine-tuned for: 20-class identity classification (CelebA celeb_ids)
• Input: RGB image (face crop), resized and normalized to 224×224
• Output: Probability distribution over 20 anonymized IDs
• Parameters: ~86M

Sources

• Base model: https://huggingface.co/google/vit-base-patch16-224
• Demo Space: https://huggingface.co/spaces/hudaakram/FaceGuard-demo
• Dataset: CelebA (community mirror on the Hub)

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Uses

Direct Use

• Research demo for identity classification with anonymized CelebA IDs
• Educational example of fine-tuning ViT for image classification

Downstream Use

• As a starting point for transfer learning to other small identity classification tasks
• As an educational reference for hackathons, workshops, or courses

Out-of-Scope Use

• ❌ Production face recognition / surveillance
• ❌ Identifying real celebrity names (dataset only provides integer IDs)
• ❌ Any high-stakes application involving privacy or personal data

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Bias, Risks, and Limitations

• Bias: CelebA contains celebrity faces, which are not representative of all demographics.
• Limitations: Trained on only 20 identities (~600 images total) → limited generalization.
• Privacy: CelebA IDs are anonymized integers, not real names. The model is not capable of returning actual identities.

Recommendation: Use strictly for research/educational purposes.

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How to Get Started

Use the code below to get started with the model.

from transformers import ViTForImageClassification, AutoImageProcessor
from PIL import Image
import torch

model_id = "hudaakram/FaceGuard-20ID-ViT"
processor = AutoImageProcessor.from_pretrained(model_id)
model = ViTForImageClassification.from_pretrained(model_id)

img = Image.open("face.jpg").convert("RGB")
inputs = processor(images=img, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits
probs = torch.softmax(logits, dim=-1)[0]

id2label = {int(k): v for k, v in model.config.id2label.items()}
top5 = probs.topk(5)
for score, idx in zip(top5.values, top5.indices):
print(f"Label {idx.item()} (celeb_id {id2label[idx.item()]}): {score:.3f}")

Training Details

Training Data

• Dataset: CelebA (top 20 identities by frequency)
• Splits: Stratified 80% train / 10% validation / 10% test
• Sizes: Train 501, Val 60, Test 77

Training Procedure

• Seed: 42
• Epochs: 4
• Batch size: 16
• Learning rate: 5e-5
• Optimizer: AdamW
• Weight decay: 0.01
• Precision: FP16 on GPU (Colab)
• Head resized: from 1000 classes → 20 classes

Preprocessing

• Images resized + center-cropped to 224×224
• Normalized to ImageNet mean/std
• Labels mapped from CelebA celeb_id → contiguous 0–19

Training Hyperparameters

• Training regime: fp16 mixed precision on GPU
• Total epochs: 4 (~3 minutes each on Colab T4)

Speeds, Sizes, Times

• Checkpoint size: ~343 MB
• Throughput: ~10 samples/sec (Colab T4)

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Evaluation

• Validation Accuracy: ~0.93
• Test Accuracy: ~0.83
• Macro AUC: (see ROC below)

Test Split Summary

Results

Confusion Matrix (normalized):

ROC Curves (one-vs-rest):

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Environmental Impact

• Hardware: Google Colab T4 GPU
• Training time: ~12 minutes total (4 epochs)
• Carbon emissions: negligible (short fine-tuning run)

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Technical Specifications

Model Architecture and Objective

• Vision Transformer (ViT-Base, patch16, 224×224)
• Objective: Cross-entropy classification across 20 labels

Compute Infrastructure

• Hardware: Google Colab T4 GPU
• Framework: PyTorch + Hugging Face Transformers

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Citation

CelebA Dataset:
Z. Liu, P. Luo, X. Wang, and X. Tang. Deep Learning Face Attributes in the Wild. ICCV 2015.

ViT:
A. Dosovitskiy et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR 2021.

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Model Card Authors

Hackathon submission by Huda Akram

Contact

• Hugging Face profile: https://huggingface.co/hudaakram