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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: vit-base-mri |
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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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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.987944228954817 |
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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-mri |
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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: 0.0690 |
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- Accuracy: 0.9879 |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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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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| 0.04 | 0.3 | 500 | 0.0828 | 0.9690 | |
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| 0.0765 | 0.59 | 1000 | 0.0623 | 0.9750 | |
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| 0.0479 | 0.89 | 1500 | 0.0453 | 0.9827 | |
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| 0.0199 | 1.18 | 2000 | 0.0524 | 0.9857 | |
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| 0.0114 | 1.48 | 2500 | 0.0484 | 0.9861 | |
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| 0.008 | 1.78 | 3000 | 0.0566 | 0.9852 | |
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| 0.0051 | 2.07 | 3500 | 0.0513 | 0.9874 | |
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| 0.0008 | 2.37 | 4000 | 0.0617 | 0.9874 | |
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| 0.0021 | 2.66 | 4500 | 0.0664 | 0.9870 | |
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| 0.0005 | 2.96 | 5000 | 0.0639 | 0.9872 | |
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| 0.001 | 3.25 | 5500 | 0.0644 | 0.9879 | |
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| 0.0004 | 3.55 | 6000 | 0.0672 | 0.9875 | |
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| 0.0003 | 3.85 | 6500 | 0.0690 | 0.9879 | |
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### Framework versions |
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- Transformers 4.20.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
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