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---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-mri
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: mriDataSet
type: imagefolder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9827025893699549
---
<!-- 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-mri
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 mriDataSet dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0453
- Accuracy: 0.9827
## 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: 3e-05
- train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.04 | 0.3 | 500 | 0.0828 | 0.9690 |
| 0.0765 | 0.59 | 1000 | 0.0623 | 0.9750 |
| 0.0479 | 0.89 | 1500 | 0.0453 | 0.9827 |
| 0.0199 | 1.18 | 2000 | 0.0524 | 0.9857 |
| 0.0114 | 1.48 | 2500 | 0.0484 | 0.9861 |
| 0.008 | 1.78 | 3000 | 0.0566 | 0.9852 |
| 0.0051 | 2.07 | 3500 | 0.0513 | 0.9874 |
| 0.0008 | 2.37 | 4000 | 0.0617 | 0.9874 |
| 0.0021 | 2.66 | 4500 | 0.0664 | 0.9870 |
| 0.0005 | 2.96 | 5000 | 0.0639 | 0.9872 |
| 0.001 | 3.25 | 5500 | 0.0644 | 0.9879 |
| 0.0004 | 3.55 | 6000 | 0.0672 | 0.9875 |
| 0.0003 | 3.85 | 6500 | 0.0690 | 0.9879 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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