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