This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1042
Accuracy: 0.9814
Precision: 0.9814
Recall: 0.9814
F1: 0.9814

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: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.006	0.22	100	0.0735	0.9863	0.9863	0.9863	0.9863
0.0044	0.45	200	0.0720	0.9882	0.9882	0.9882	0.9882
0.3589	0.67	300	0.5454	0.8902	0.8902	0.8902	0.8902
0.401	0.89	400	0.6406	0.8676	0.8676	0.8676	0.8676
0.1851	1.11	500	0.4838	0.8912	0.8912	0.8912	0.8912
0.1116	1.34	600	0.3375	0.9245	0.9245	0.9245	0.9245
0.2359	1.56	700	0.4032	0.9059	0.9059	0.9059	0.9059
0.062	1.78	800	0.2356	0.9549	0.9549	0.9549	0.9549
0.0221	2.0	900	0.2307	0.9559	0.9559	0.9559	0.9559
0.0052	2.23	1000	0.1620	0.9676	0.9676	0.9676	0.9676
0.0277	2.45	1100	0.1881	0.9676	0.9676	0.9676	0.9676
0.0025	2.67	1200	0.1483	0.9735	0.9735	0.9735	0.9735
0.0078	2.9	1300	0.1199	0.9794	0.9794	0.9794	0.9794
0.002	3.12	1400	0.1343	0.9755	0.9755	0.9755	0.9755
0.0035	3.34	1500	0.1247	0.9775	0.9775	0.9775	0.9775
0.0245	3.56	1600	0.1116	0.9775	0.9775	0.9775	0.9775
0.0015	3.79	1700	0.1099	0.9775	0.9775	0.9775	0.9775
0.0013	4.01	1800	0.1089	0.9804	0.9804	0.9804	0.9804
0.0014	4.23	1900	0.1081	0.9804	0.9804	0.9804	0.9804
0.0013	4.45	2000	0.1076	0.9804	0.9804	0.9804	0.9804
0.0012	4.68	2100	0.1075	0.9804	0.9804	0.9804	0.9804
0.0013	4.9	2200	0.1042	0.9814	0.9814	0.9814	0.9814

Framework versions

Transformers 4.39.3
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

KietZer0/ViT_Flower102_4

ViT_Flower102_4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

KietZer0/
ViT_Flower102_4