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README.md
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---
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license: apache-2.0
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tags:
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- image-classification
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- generated_from_trainer
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datasets:
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- cats_vs_dogs
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metrics:
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- accuracy
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model-index:
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- name: vit-base-cats-vs-dogs
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: cats_vs_dogs
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type: cats_vs_dogs
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9937357630979499
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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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# vit-base-cats-vs-dogs
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cats_vs_dogs dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0182
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- Accuracy: 0.9937
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 1337
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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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- num_epochs: 5.0
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.1177 | 1.0 | 622 | 0.0473 | 0.9832 |
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| 0.057 | 2.0 | 1244 | 0.0362 | 0.9883 |
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| 0.0449 | 3.0 | 1866 | 0.0261 | 0.9886 |
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| 0.066 | 4.0 | 2488 | 0.0248 | 0.9923 |
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| 0.0328 | 5.0 | 3110 | 0.0182 | 0.9937 |
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### Framework versions
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- Transformers 4.13.0.dev0
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- Pytorch 1.8.1+cu111
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- Datasets 1.15.1
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- Tokenizers 0.10.3
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