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jialicheng/
unlearn-so_cifar10_swin-base_salun_4_42

Name: unlearn-so_cifar10_swin-base_salun_4_42
Availability: InStock

generated_from_trainer

microsoft/swin-base-patch4-window7-224

finetune

apache-2.0

endpoints_compatible

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42

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: 0.0875
Accuracy: 0.9824
Dt Accuracy: 0.9824
Df Accuracy: 0.7915
Unlearn Overall Accuracy: 0.4706
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: 42
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	0.1681	0.879	0.3600	0.3600	None
0.5	2.0	782	0.1559	0.876	0.3642	0.3642	None
0.4449	3.0	1173	0.1099	0.8955	0.3387	0.3387	None
0.4007	4.0	1564	0.1078	0.905	0.3257	0.3257	None
0.4007	5.0	1955	0.0963	0.8975	0.3362	0.3362	None
0.3698	6.0	2346	0.0893	0.9075	0.3225	0.3225	None
0.3309	7.0	2737	0.0855	0.887	0.3507	0.3507	None
0.2925	8.0	3128	0.1027	0.8485	0.4013	0.4013	None
0.2629	9.0	3519	0.0896	0.815	0.4428	0.4428	None
0.2629	10.0	3910	0.0875	0.7915	0.4706	0.4706	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_4_42

42

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jialicheng/
unlearn-so_cifar10_swin-base_salun_4_42