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

sourceHugging Faceapache-2.0updated 4y agoView on Hugging Face

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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
—trainbatchsize: 32
—evalbatchsize: 32
—seed: 17
—gradientaccumulationsteps: 4
—totaltrainbatch_size: 128
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—lrschedulerwarmup_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