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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: celebrity-classifier |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Celebrity Classifier |
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## Model description |
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This model classifies a face to a celebrity. It is trained on [ares1123/celebrity_dataset](https://huggingface.co/datasets/ares1123/celebrity_dataset) dataset and fine-tuned on [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k). |
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## Dataset description |
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[ares1123/celebrity_dataset](https://huggingface.co/datasets/ares1123/celebrity_dataset) |
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Top 1000 celebrities. 18,184 images. 256x256. Square cropped to face. |
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### How to use |
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```python |
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from transformers import pipeline |
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# Initialize image classification pipeline |
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pipe = pipeline("image-classification", model="tonyassi/celebrity-classifier") |
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# Perform classification |
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result = pipe('image.png') |
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# Print results |
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print(result) |
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``` |
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## Training and evaluation data |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9089 |
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- Accuracy: 0.7982 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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