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: 1.1926
Accuracy: 0.5938
Precision: 0.6599
Recall: 0.5938
F1: 0.5920

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: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
No log	1.0	80	1.6474	0.3375	0.3120	0.3375	0.2259
No log	2.0	160	1.4434	0.4625	0.5606	0.4625	0.4112
No log	3.0	240	1.3266	0.4875	0.5296	0.4875	0.4516
No log	4.0	320	1.2547	0.5375	0.5836	0.5375	0.5342
No log	5.0	400	1.2195	0.5875	0.6815	0.5875	0.5900
No log	6.0	480	1.1895	0.5563	0.5709	0.5563	0.5424
1.2914	7.0	560	1.1572	0.5437	0.5607	0.5437	0.5431
1.2914	8.0	640	1.1822	0.5563	0.5602	0.5563	0.5515
1.2914	9.0	720	1.2712	0.55	0.5695	0.55	0.5530
1.2914	10.0	800	1.2196	0.5625	0.5701	0.5625	0.5559
1.2914	11.0	880	1.2460	0.5312	0.5584	0.5312	0.5357
1.2914	12.0	960	1.2473	0.5563	0.5710	0.5563	0.5553
0.5247	13.0	1040	1.2438	0.575	0.5908	0.575	0.5761
0.5247	14.0	1120	1.3033	0.5312	0.5391	0.5312	0.5305
0.5247	15.0	1200	1.2928	0.5625	0.5861	0.5625	0.5673

Framework versions

Transformers 4.41.2
Pytorch 2.3.0
Datasets 2.19.1
Tokenizers 0.19.1

JamesJayamuni/emotion_classification_v1

emotion_classification_v1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

JamesJayamuni/
emotion_classification_v1