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metadata
license: apache-2.0
base_model: DunnBC22/vit-base-patch16-224-in21k_covid_19_ct_scans
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: vit-base-patch16-224-in21k_covid_19_ct_scans-finetuned-RCC
    results: []

vit-base-patch16-224-in21k_covid_19_ct_scans-finetuned-RCC

This model is a fine-tuned version of DunnBC22/vit-base-patch16-224-in21k_covid_19_ct_scans on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3235
  • Accuracy: 0.9032
  • Precision: 0.9032
  • Recall: 1.0
  • F1: 0.4746
  • Auc: 0.5

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 5

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Auc
No log 1.0 7 0.3327 0.9032 0.9032 1.0 0.4746 0.5
0.3866 2.0 14 0.3213 0.9032 0.9032 1.0 0.4746 0.5
0.2647 3.0 21 0.3226 0.9032 0.9032 1.0 0.4746 0.5
0.2647 4.0 28 0.3246 0.9032 0.9032 1.0 0.4746 0.5
0.2593 5.0 35 0.3235 0.9032 0.9032 1.0 0.4746 0.5

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.0.0+cu117
  • Datasets 2.19.1
  • Tokenizers 0.19.1