hq_fer2013notestaugM

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: 0.8297
• Accuracy: 0.6998
• Precision: 0.7022
• Recall: 0.6998
• F1: 0.6999

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: 1e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 17
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.2858	1.0	353	1.2814	0.5545	0.5432	0.5545	0.5122
1.0247	2.0	706	1.0343	0.6288	0.6235	0.6288	0.6136
0.9403	3.0	1059	0.9500	0.6607	0.6592	0.6607	0.6522
0.8501	4.0	1412	0.8971	0.6803	0.6761	0.6803	0.6760
0.8148	5.0	1765	0.8733	0.6857	0.6881	0.6857	0.6854
0.7898	6.0	2118	0.8526	0.6913	0.6911	0.6913	0.6888
0.7074	7.0	2471	0.8408	0.6959	0.6971	0.6959	0.6953
0.7273	8.0	2824	0.8361	0.6980	0.6971	0.6980	0.6949
0.6982	9.0	3177	0.8297	0.6998	0.7022	0.6998	0.6999
0.6994	10.0	3530	0.8287	0.6998	0.7002	0.6998	0.6991

Framework versions

• Transformers 4.27.0.dev0
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.2