metadata

license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
  - name: vit-base-mri
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: mriDataSet
          type: imagefolder
          args: default
        metrics:
          - type: accuracy
            value: 0.9827025893699549
            name: Accuracy

vit-base-mri

This model is a fine-tuned version of 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