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: 1.2383
Accuracy: 0.6

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
2.0769	1.0	10	2.0617	0.1812
2.0383	2.0	20	2.0104	0.3
1.9423	3.0	30	1.8932	0.425
1.7923	4.0	40	1.7442	0.475
1.6547	5.0	50	1.6047	0.4875
1.5297	6.0	60	1.5184	0.5437
1.4345	7.0	70	1.4392	0.5625
1.337	8.0	80	1.3847	0.5875
1.2722	9.0	90	1.3442	0.55
1.217	10.0	100	1.3058	0.5625
1.1497	11.0	110	1.2914	0.55
1.0977	12.0	120	1.2377	0.6125
1.0507	13.0	130	1.2253	0.5687
1.0268	14.0	140	1.2269	0.5938
0.967	15.0	150	1.2260	0.5938
0.9269	16.0	160	1.2421	0.5687
0.9102	17.0	170	1.2218	0.5687
0.8883	18.0	180	1.2207	0.5687
0.8633	19.0	190	1.1933	0.6062
0.8557	20.0	200	1.1830	0.575

Framework versions

Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3

awrysfab/emotion_classification

emotion_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

awrysfab/
emotion_classification