beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-finetuned-SFEW-7e-05 – AI Model by lixiqi | AlphaNeural AI

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lixiqi/
beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-finetuned-SFEW-7e-05

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image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05-finetuned-FER2013-7e-05

This model is a fine-tuned version of lixiqi/beit-base-patch16-224-pt22k-ft22k-finetuned-FER2013-7e-05 on the image_folder dataset. It achieves the following results on the evaluation set:

Loss: 1.5659
Accuracy: 0.5260

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: 7e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
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
1.6537	0.97	14	1.4980	0.4683
1.4325	1.97	28	1.4777	0.5040
1.1532	2.97	42	1.5007	0.4960
1.0428	3.97	56	1.5480	0.4890
0.8716	4.97	70	1.5659	0.5260
0.892	5.97	84	1.6132	0.4960
0.8109	6.97	98	1.5895	0.5167
0.7413	7.97	112	1.6271	0.5202
0.765	8.97	126	1.5991	0.5040
0.6575	9.97	140	1.6041	0.4960

Framework versions

Transformers 4.20.1
Pytorch 1.11.0
Datasets 2.1.0
Tokenizers 0.12.1