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
- Downloads last month
- 389
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for tonyassi/celebrity-classifier
Base model
google/vit-base-patch16-224-in21k