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license: apache-2.0 |
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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: vit-base-letter |
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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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# vit-base-letter |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0559 |
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- Accuracy: 0.9865 |
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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: 16 |
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- seed: 42 |
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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: 4 |
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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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| 0.5539 | 0.12 | 100 | 0.5576 | 0.9308 | |
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| 0.2688 | 0.25 | 200 | 0.2371 | 0.9665 | |
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| 0.1568 | 0.37 | 300 | 0.1829 | 0.9688 | |
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| 0.1684 | 0.49 | 400 | 0.1611 | 0.9662 | |
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| 0.1584 | 0.62 | 500 | 0.1340 | 0.9673 | |
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| 0.1569 | 0.74 | 600 | 0.1933 | 0.9531 | |
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| 0.0992 | 0.86 | 700 | 0.1031 | 0.9781 | |
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| 0.0573 | 0.98 | 800 | 0.1024 | 0.9781 | |
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| 0.0359 | 1.11 | 900 | 0.0950 | 0.9804 | |
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| 0.0961 | 1.23 | 1000 | 0.1200 | 0.9723 | |
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| 0.0334 | 1.35 | 1100 | 0.0995 | 0.975 | |
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| 0.0855 | 1.48 | 1200 | 0.0791 | 0.9815 | |
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| 0.0902 | 1.6 | 1300 | 0.0981 | 0.9765 | |
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| 0.0583 | 1.72 | 1400 | 0.1192 | 0.9712 | |
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| 0.0683 | 1.85 | 1500 | 0.0692 | 0.9846 | |
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| 0.1188 | 1.97 | 1600 | 0.0931 | 0.9785 | |
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| 0.0366 | 2.09 | 1700 | 0.0919 | 0.9804 | |
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| 0.0276 | 2.21 | 1800 | 0.0667 | 0.9846 | |
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| 0.0309 | 2.34 | 1900 | 0.0599 | 0.9858 | |
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| 0.0183 | 2.46 | 2000 | 0.0892 | 0.9769 | |
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| 0.0431 | 2.58 | 2100 | 0.0663 | 0.985 | |
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| 0.0424 | 2.71 | 2200 | 0.0643 | 0.9862 | |
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| 0.0453 | 2.83 | 2300 | 0.0646 | 0.9862 | |
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| 0.0528 | 2.95 | 2400 | 0.0550 | 0.985 | |
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| 0.0045 | 3.08 | 2500 | 0.0579 | 0.9846 | |
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| 0.007 | 3.2 | 2600 | 0.0517 | 0.9885 | |
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| 0.0048 | 3.32 | 2700 | 0.0584 | 0.9865 | |
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| 0.019 | 3.44 | 2800 | 0.0560 | 0.9873 | |
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| 0.0038 | 3.57 | 2900 | 0.0515 | 0.9881 | |
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| 0.0219 | 3.69 | 3000 | 0.0527 | 0.9881 | |
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| 0.0117 | 3.81 | 3100 | 0.0523 | 0.9888 | |
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| 0.0035 | 3.94 | 3200 | 0.0559 | 0.9865 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.2 |
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