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: 0.0080
Accuracy: 0.9979
Precision: 0.9982
Recall: 0.9976
F1: 0.9979

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: 128
eval_batch_size: 128
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 512
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
0.015	1.0	101	0.0229	0.9938	0.9928	0.9929	0.9928
0.0177	2.0	203	0.0316	0.9913	0.9928	0.9880	0.9903
0.0177	3.0	304	0.0127	0.9958	0.9954	0.9955	0.9954
0.0145	4.0	406	0.0129	0.9967	0.9970	0.9960	0.9965
0.0101	4.98	505	0.0080	0.9979	0.9982	0.9976	0.9979

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3

hyeongjin99/vit-base-aihub_model

vit-base-aihub_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hyeongjin99/
vit-base-aihub_model