finetuned-fer2013-balanced – AI Model by Piro17 | AlphaNeural AI

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Piro17/
finetuned-fer2013-balanced

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finetuned-fer2013-balanced

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.0183
Accuracy: 0.6362
Precision: 0.6312
Recall: 0.6362
F1: 0.6310

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: 16
eval_batch_size: 16
seed: 17
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
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
1.3969	1.0	267	1.3735	0.4996	0.4776	0.4996	0.4436
1.2341	2.0	534	1.2239	0.5442	0.5430	0.5442	0.5108
1.1363	3.0	801	1.1585	0.5758	0.5715	0.5758	0.5638
1.0894	4.0	1068	1.1087	0.5912	0.5827	0.5912	0.5706
1.0666	5.0	1335	1.0655	0.6184	0.6111	0.6184	0.6082
0.9219	6.0	1602	1.0520	0.6233	0.6153	0.6233	0.6136
0.943	7.0	1869	1.0331	0.6299	0.6238	0.6299	0.6231
0.8906	8.0	2136	1.0238	0.6318	0.6252	0.6318	0.6239
0.8854	9.0	2403	1.0196	0.6341	0.6313	0.6341	0.6298
0.8991	10.0	2670	1.0183	0.6362	0.6312	0.6362	0.6310

Framework versions

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