| ---
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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-RU5-10-8
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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: 0.7833333333333333
|
| ---
|
|
|
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| should probably proofread and complete it, then remove this comment. -->
|
|
|
| # vit-base-patch16-224-RU5-10-8
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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: 0.6773
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| - Accuracy: 0.7833
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|
|
| ## Model description
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|
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| More information needed
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|
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| ## Intended uses & limitations
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|
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| More information needed
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|
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| ## Training and evaluation data
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|
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| More information needed
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|
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| ## Training procedure
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|
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| ### Training hyperparameters
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|
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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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|
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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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| | 1.3605 | 0.95 | 14 | 1.2370 | 0.5167 |
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| | 1.2314 | 1.97 | 29 | 1.0511 | 0.6833 |
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| | 0.968 | 2.98 | 44 | 0.8919 | 0.65 |
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| | 0.8135 | 4.0 | 59 | 0.7702 | 0.7667 |
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| | 0.616 | 4.95 | 73 | 0.7533 | 0.75 |
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| | 0.5167 | 5.97 | 88 | 0.6773 | 0.7833 |
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| | 0.4063 | 6.98 | 103 | 0.6974 | 0.75 |
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| | 0.3401 | 8.0 | 118 | 0.7438 | 0.75 |
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| | 0.3007 | 8.95 | 132 | 0.6646 | 0.7833 |
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| | 0.3154 | 9.49 | 140 | 0.6819 | 0.7833 |
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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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|
|
