| --- |
| license: apache-2.0 |
| tags: |
| - generated_from_trainer |
| base_model: google/vit-base-patch16-224 |
| metrics: |
| - accuracy |
| model-index: |
| - name: model |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # model |
|
|
| This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5652 |
| - Accuracy: 0.7486 |
| - Roc Auc: 0.7023 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 5e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 4 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Roc Auc | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:| |
| | 0.5665 | 0.14 | 50 | 0.5829 | 0.71 | 0.6554 | |
| | 0.5428 | 0.29 | 100 | 0.6787 | 0.71 | 0.6873 | |
| | 0.5793 | 0.43 | 150 | 0.5501 | 0.7429 | 0.6910 | |
| | 0.567 | 0.57 | 200 | 0.5489 | 0.7443 | 0.6951 | |
| | 0.5427 | 0.71 | 250 | 0.5758 | 0.73 | 0.6809 | |
| | 0.5022 | 0.86 | 300 | 0.5784 | 0.7229 | 0.6489 | |
| | 0.5415 | 1.0 | 350 | 0.5530 | 0.7429 | 0.6791 | |
| | 0.5731 | 1.14 | 400 | 0.5440 | 0.7457 | 0.6955 | |
| | 0.4746 | 1.29 | 450 | 0.5632 | 0.7486 | 0.6916 | |
| | 0.6076 | 1.43 | 500 | 0.5356 | 0.7571 | 0.7089 | |
| | 0.4674 | 1.57 | 550 | 0.5477 | 0.7471 | 0.7247 | |
| | 0.546 | 1.71 | 600 | 0.5774 | 0.7457 | 0.7038 | |
| | 0.5776 | 1.86 | 650 | 0.5367 | 0.7443 | 0.7139 | |
| | 0.4802 | 2.0 | 700 | 0.5418 | 0.7429 | 0.7038 | |
| | 0.5612 | 2.14 | 750 | 0.6319 | 0.6714 | 0.6911 | |
| | 0.4281 | 2.29 | 800 | 0.5550 | 0.7443 | 0.6951 | |
| | 0.518 | 2.43 | 850 | 0.6038 | 0.7014 | 0.6743 | |
| | 0.505 | 2.57 | 900 | 0.5480 | 0.7486 | 0.7036 | |
| | 0.4689 | 2.71 | 950 | 0.5304 | 0.7571 | 0.7191 | |
| | 0.5685 | 2.86 | 1000 | 0.5453 | 0.7557 | 0.7009 | |
| | 0.4624 | 3.0 | 1050 | 0.6102 | 0.7386 | 0.7176 | |
| | 0.5246 | 3.14 | 1100 | 0.5674 | 0.7243 | 0.6932 | |
| | 0.4601 | 3.29 | 1150 | 0.5538 | 0.74 | 0.7035 | |
| | 0.4663 | 3.43 | 1200 | 0.5531 | 0.75 | 0.7036 | |
| | 0.4084 | 3.57 | 1250 | 0.5787 | 0.7429 | 0.6901 | |
| | 0.3992 | 3.71 | 1300 | 0.5691 | 0.7386 | 0.6965 | |
| | 0.4385 | 3.86 | 1350 | 0.5701 | 0.7457 | 0.7012 | |
| | 0.5024 | 4.0 | 1400 | 0.5652 | 0.7486 | 0.7023 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.39.1 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
|
|
