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: 1.4174
Accuracy: 0.525

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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0777	1.0	10	2.0583	0.1812
2.0139	2.0	20	1.9850	0.2687
1.8654	3.0	30	1.8583	0.3063
1.7044	4.0	40	1.7314	0.3937
1.5957	5.0	50	1.6253	0.4125
1.5016	6.0	60	1.5818	0.3812
1.4279	7.0	70	1.5329	0.45
1.347	8.0	80	1.5491	0.425
1.3019	9.0	90	1.4662	0.5125
1.236	10.0	100	1.4375	0.5
1.1922	11.0	110	1.4149	0.5062
1.1551	12.0	120	1.4065	0.5125
1.1501	13.0	130	1.3861	0.5125
1.1258	14.0	140	1.3940	0.5312
1.1036	15.0	150	1.4022	0.5125

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.19.1

diwa02/emotion_classification

emotion_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

diwa02/
emotion_classification