emotion_classification

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: 1.2002
• Accuracy: 0.6438

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
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 12
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	40	1.9756	0.2313
No log	2.0	80	1.6788	0.3937
No log	3.0	120	1.5219	0.5375
No log	4.0	160	1.4542	0.45
No log	5.0	200	1.3923	0.5
No log	6.0	240	1.3595	0.4437
No log	7.0	280	1.3111	0.5125
No log	8.0	320	1.2050	0.5625
No log	9.0	360	1.2387	0.5437
No log	10.0	400	1.2847	0.5437
No log	11.0	440	1.2048	0.5625
No log	12.0	480	1.2270	0.5563
1.0855	13.0	520	1.2058	0.5875
1.0855	14.0	560	1.1999	0.5625
1.0855	15.0	600	1.2032	0.5687

Framework versions

• Transformers 4.41.1
• Pytorch 2.1.2
• Datasets 2.19.1
• Tokenizers 0.19.1