Celebrity Classifier

🚀 Celebrity Classifier

This model is designed to classify faces into corresponding celebrities, offering a practical solution for face recognition tasks.

🚀 Quick Start

This model classifies a face to a celebrity. It is trained on tonyassi/celebrity-1000 dataset and fine-tuned on google/vit-base-patch16-224-in21k.

✨ Features

• Classify faces into corresponding celebrities.
• Trained on a specific dataset and fine - tuned on a well - known base model.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

from transformers import pipeline

# Initialize image classification pipeline
pipe = pipeline("image-classification", model="tonyassi/celebrity-classifier")

# Perform classification
result = pipe('image.png')

# Print results
print(result)

📚 Documentation

Dataset description

tonyassi/celebrity-1000 Top 1000 celebrities. 18,184 images. 256x256. Square cropped to face.

Training and evaluation data

It achieves the following results on the evaluation set:

• Loss: 0.9089
• Accuracy: 0.7982

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 20

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0

🔧 Technical Details

The model is fine - tuned on google/vit-base-patch16-224-in21k and trained on the tonyassi/celebrity-1000 dataset. Specific hyperparameters are used during training to achieve certain accuracy and loss results on the evaluation set.

📄 License

The model is under the Apache - 2.0 license.