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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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- 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: turcoins-classifier
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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: hsyntemiz--turcoins
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split: test
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args: hsyntemiz--turcoins
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9548611111111112
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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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# turcoins-classifier
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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 imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1763
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- Accuracy: 0.9549
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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: 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: 30
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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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| 1.9277 | 1.0 | 146 | 1.9660 | 0.7726 |
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| 1.6627 | 2.0 | 292 | 1.7154 | 0.7917 |
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| 1.4071 | 2.99 | 438 | 1.4120 | 0.8079 |
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| 1.09 | 4.0 | 585 | 1.1225 | 0.8362 |
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| 0.8086 | 5.0 | 731 | 0.8917 | 0.8675 |
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| 0.7636 | 6.0 | 877 | 0.7596 | 0.8709 |
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| 0.611 | 6.99 | 1023 | 0.6493 | 0.8883 |
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| 0.4605 | 8.0 | 1170 | 0.5899 | 0.8872 |
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| 0.37 | 9.0 | 1316 | 0.4978 | 0.9045 |
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| 0.3882 | 10.0 | 1462 | 0.4424 | 0.9132 |
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| 0.3139 | 10.99 | 1608 | 0.3969 | 0.9115 |
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| 0.3178 | 12.0 | 1755 | 0.3525 | 0.9294 |
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| 0.2796 | 13.0 | 1901 | 0.3552 | 0.9161 |
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| 0.2571 | 14.0 | 2047 | 0.3189 | 0.9265 |
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| 0.2481 | 14.99 | 2193 | 0.2945 | 0.9358 |
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| 0.1875 | 16.0 | 2340 | 0.2647 | 0.9392 |
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| 0.1861 | 17.0 | 2486 | 0.2404 | 0.9410 |
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| 0.1839 | 18.0 | 2632 | 0.2556 | 0.9421 |
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| 0.173 | 18.99 | 2778 | 0.2387 | 0.9462 |
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| 0.1837 | 20.0 | 2925 | 0.2049 | 0.9485 |
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| 0.1724 | 21.0 | 3071 | 0.2065 | 0.9525 |
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| 0.1399 | 22.0 | 3217 | 0.2089 | 0.9404 |
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| 0.1696 | 22.99 | 3363 | 0.1957 | 0.9497 |
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| 0.1405 | 24.0 | 3510 | 0.1848 | 0.9554 |
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| 0.1009 | 25.0 | 3656 | 0.1912 | 0.9520 |
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| 0.1126 | 26.0 | 3802 | 0.1717 | 0.9560 |
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| 0.1336 | 26.99 | 3948 | 0.1699 | 0.9589 |
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| 0.1046 | 28.0 | 4095 | 0.1600 | 0.9601 |
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| 0.126 | 29.0 | 4241 | 0.1839 | 0.9520 |
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| 0.0882 | 29.95 | 4380 | 0.1763 | 0.9549 |
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### Framework versions
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- Transformers 4.28.1
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- Pytorch 2.0.0+cu117
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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