vit-base-patch16-224-in21k_covid_19_ct_scans-finetuned-RCC – AI Model by synergyai-jaeung | AlphaNeural AI

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synergyai-jaeung/
vit-base-patch16-224-in21k_covid_19_ct_scans-finetuned-RCC

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image-classification

generated_from_trainer

DunnBC22/vit-base-patch16-224-in21k_covid_19_ct_scans

autotrain_compatible

endpoints_compatible

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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