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: 1.2998
F1: 0.3729

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: 16
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	F1
0.1696	0.79	100	1.1385	0.352
0.08	1.59	200	0.9071	0.3774
0.0928	2.38	300	1.1181	0.3454
0.0189	3.17	400	0.8262	0.3425
0.0728	3.97	500	0.9647	0.3747
0.0756	4.76	600	0.6097	0.4776
0.0018	5.56	700	1.3900	0.3652
0.002	6.35	800	0.7498	0.4606
0.0304	7.14	900	1.4367	0.3666
0.0024	7.94	1000	1.5714	0.3041
0.0463	8.73	1100	0.8038	0.4016
0.0014	9.52	1200	0.7175	0.4795
0.0015	10.32	1300	1.0347	0.3959
0.0009	11.11	1400	1.3881	0.3670
0.0131	11.9	1500	1.0780	0.4044
0.0007	12.7	1600	0.9834	0.4255
0.0011	13.49	1700	1.0753	0.4033
0.0007	14.29	1800	1.1514	0.3989
0.0007	15.08	1900	1.2373	0.3769
0.0007	15.87	2000	1.2998	0.3729

Framework versions

Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.10.1
Tokenizers 0.13.2

MaxP/vit-base-riego

vit-base-riego

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MaxP/
vit-base-riego