Celebrity Classifier

Model description

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.

Dataset description

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

How to use

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)

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
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