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# Celebrity Classifier
## Model description
This model classifies a face to a celebrity. It is trained on [tonyassi/celebrity-1000](https://huggingface.co/datasets/tonyassi/celebrity-1000) dataset and fine-tuned on [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k).

## Dataset description
[tonyassi/celebrity-1000](https://huggingface.co/datasets/tonyassi/celebrity-1000)
Top 1000 celebrities. 18,184 images. 256x256. Square cropped to face.

### How to use
```python
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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "celebrity-classifier", "results": []}]}
transformers tensorboard safetensors vit image-classification generated_from_trainer base_model:google/vit-base-patch16-224-in21k license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us