Instructions to use tonyassi/celebrity-classifier-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tonyassi/celebrity-classifier-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="tonyassi/celebrity-classifier-1") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("tonyassi/celebrity-classifier-1") model = AutoModelForImageClassification.from_pretrained("tonyassi/celebrity-classifier-1") - Notebooks
- Google Colab
- Kaggle
Celebrity Classifier 1
Model description
Trained on tonyassi/celeb-1 dataset.
This model is a fine-tuned version of google/vit-base-patch16-224-in21k.
Training and evaluation data
It achieves the following results on the evaluation set:
- Loss: 0.8899
- Accuracy: 0.8163
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 80
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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Model tree for tonyassi/celebrity-classifier-1
Base model
google/vit-base-patch16-224-in21k