10E-affecthq-fer-balanced-w0.1-jitter-jiggle – AI Model by Piro17 | AlphaNeural AI

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Piro17/
10E-affecthq-fer-balanced-w0.1-jitter-jiggle

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image-classification

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autotrain_compatible

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10E-affecthq-fer-balanced-w0.1-jitter-jiggle

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: 1.0926
Accuracy: 0.6094
Precision: 0.5984
Recall: 0.6094
F1: 0.5985

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
1.7434	1.0	133	1.7100	0.3920	0.5334	0.3920	0.2829
1.3586	2.0	266	1.3433	0.5115	0.4915	0.5115	0.4535
1.2118	3.0	399	1.2464	0.5457	0.5288	0.5457	0.5084
1.1762	4.0	532	1.1858	0.5724	0.5615	0.5724	0.5435
1.1222	5.0	665	1.1502	0.5850	0.5704	0.5850	0.5601
1.074	6.0	798	1.1300	0.5963	0.5841	0.5963	0.5800
1.0299	7.0	931	1.1119	0.6014	0.5922	0.6014	0.5880
0.9919	8.0	1064	1.1001	0.6028	0.5907	0.6028	0.5890
0.9761	9.0	1197	1.0943	0.6075	0.5966	0.6075	0.5950
0.9769	10.0	1330	1.0926	0.6094	0.5984	0.6094	0.5985

Framework versions

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