metadata

license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: vit-base-patch16-224-in21k_covid_19_ct_scans
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9010416666666666
          - name: F1
            type: f1
            value: 0.473972602739726
          - name: Recall
            type: recall
            value: 0.9942528735632183
          - name: Precision
            type: precision
            value: 0.9057591623036649

vit-base-patch16-224-in21k_covid_19_ct_scans

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.6385
Accuracy: 0.9010
F1: 0.4740
Auc: 0.4971
Recall: 0.9943
Precision: 0.9058

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: 0.0002
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Auc	Recall	Precision
0.7218	1.0	55	0.3383	0.9062	0.4754	0.5	1.0	0.9062
0.7218	2.0	110	0.3823	0.9062	0.4754	0.5	1.0	0.9062
0.7218	3.0	165	0.3957	0.9062	0.4754	0.5	1.0	0.9062
0.7218	4.0	220	0.4485	0.9062	0.4754	0.5	1.0	0.9062
0.7218	5.0	275	0.4786	0.8958	0.4725	0.4943	0.9885	0.9053
0.7218	6.0	330	0.5316	0.9010	0.4740	0.4971	0.9943	0.9058
0.7218	7.0	385	0.5539	0.9010	0.4740	0.4971	0.9943	0.9058
0.7218	8.0	440	0.5800	0.9010	0.4740	0.4971	0.9943	0.9058
0.7218	9.0	495	0.5977	0.9010	0.4740	0.4971	0.9943	0.9058
0.0987	10.0	550	0.6110	0.9010	0.4740	0.4971	0.9943	0.9058
0.0987	11.0	605	0.6211	0.9010	0.4740	0.4971	0.9943	0.9058
0.0987	12.0	660	0.6288	0.9010	0.4740	0.4971	0.9943	0.9058
0.0987	13.0	715	0.6341	0.9010	0.4740	0.4971	0.9943	0.9058
0.0987	14.0	770	0.6374	0.9010	0.4740	0.4971	0.9943	0.9058
0.0987	15.0	825	0.6385	0.9010	0.4740	0.4971	0.9943	0.9058

Framework versions

Transformers 4.41.1
Pytorch 2.0.0+cu117
Datasets 2.19.1
Tokenizers 0.19.1