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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