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jialicheng/
unlearn-so_cifar10_swin-base_salun_2_21

Name: unlearn-so_cifar10_swin-base_salun_2_21
Availability: InStock

generated_from_trainer

microsoft/swin-base-patch4-window7-224

finetune

apache-2.0

endpoints_compatible

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21

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the cifar10 dataset. It achieves the following results on the evaluation set:

Loss: 0.0761
Accuracy: 0.9838
Dt Accuracy: 0.9838
Df Accuracy: 0.754
Unlearn Overall Accuracy: 0.5132
Unlearn Time: None

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: 0.0001
train_batch_size: 128
eval_batch_size: 256
seed: 21
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Overall Accuracy	Unlearn Overall Accuracy	Time
No log	1.0	391	0.1170	0.876	0.3644	0.3644	None
0.4264	2.0	782	0.0885	0.88	0.3597	0.3597	None
0.3758	3.0	1173	0.1003	0.899	0.3337	0.3337	None
0.3368	4.0	1564	0.1089	0.892	0.3432	0.3432	None
0.3368	5.0	1955	0.0911	0.9	0.3327	0.3327	None
0.3037	6.0	2346	0.0853	0.874	0.3681	0.3681	None
0.2685	7.0	2737	0.0818	0.846	0.4045	0.4045	None
0.2363	8.0	3128	0.0807	0.837	0.4158	0.4158	None
0.2077	9.0	3519	0.0779	0.772	0.4930	0.4930	None
0.2077	10.0	3910	0.0761	0.754	0.5132	0.5132	None

Framework versions

Transformers 4.48.0
Pytorch 2.3.0+cu121
Datasets 2.19.0
Tokenizers 0.21.0

jialicheng/unlearn-so_cifar10_swin-base_salun_2_21

21

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jialicheng/
unlearn-so_cifar10_swin-base_salun_2_21