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: 1.1554
Accuracy: 0.5938

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.2477	1.0	10	1.3618	0.5625
1.2002	2.0	20	1.3367	0.5625
1.111	3.0	30	1.3178	0.5312
1.0286	4.0	40	1.2215	0.5625
0.9376	5.0	50	1.2117	0.5437
0.8948	6.0	60	1.2304	0.5625
0.8234	7.0	70	1.1634	0.5563
0.8069	8.0	80	1.2422	0.5563
0.7146	9.0	90	1.2053	0.5563
0.709	10.0	100	1.1887	0.575
0.6404	11.0	110	1.2208	0.5563
0.6301	12.0	120	1.2319	0.5687
0.6107	13.0	130	1.1684	0.6
0.5825	14.0	140	1.1837	0.5813
0.5454	15.0	150	1.1818	0.5687
0.5517	16.0	160	1.1974	0.55
0.4989	17.0	170	1.1304	0.6
0.4875	18.0	180	1.2277	0.5375
0.4881	19.0	190	1.1363	0.5875
0.4951	20.0	200	1.1540	0.6062

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

abelkrw/emotion_classification

emotion_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

abelkrw/
emotion_classification