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---
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license: apache-2.0
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base_model: google/vit-base-patch16-224
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: vit-base-patch16-224-RU4-10
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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: imagefolder
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type: imagefolder
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config: default
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split: validation
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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: 0.8333333333333334
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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-patch16-224-RU4-10
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5903
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- Accuracy: 0.8333
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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: 5.5e-05
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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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: 0.05
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- num_epochs: 10
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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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| 1.37 | 0.99 | 19 | 1.1940 | 0.6167 |
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| 1.1393 | 1.97 | 38 | 0.9231 | 0.7 |
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| 0.8115 | 2.96 | 57 | 0.7924 | 0.7667 |
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| 0.5507 | 4.0 | 77 | 0.6691 | 0.75 |
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| 0.4093 | 4.99 | 96 | 0.6462 | 0.8167 |
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| 0.2869 | 5.97 | 115 | 0.5903 | 0.8333 |
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| 0.2347 | 6.96 | 134 | 0.7096 | 0.7333 |
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| 0.2148 | 8.0 | 154 | 0.6362 | 0.7833 |
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| 0.1868 | 8.99 | 173 | 0.6496 | 0.8 |
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| 0.1977 | 9.87 | 190 | 0.6368 | 0.7667 |
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 2.1.2+cu118
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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