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jialicheng/
unlearn-so_cifar10_swin-base_salun_6_42

Name: unlearn-so_cifar10_swin-base_salun_6_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: 0.1136
Accuracy: 0.9786
Dt Accuracy: 0.9786
Df Accuracy: 0.8007
Unlearn Overall Accuracy: 0.4594
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	0.1626	0.8883	0.3478	0.3478	None
0.5561	2.0	782	0.1223	0.9097	0.3190	0.3190	None
0.4947	3.0	1173	0.1397	0.902	0.3297	0.3297	None
0.4605	4.0	1564	0.1274	0.9097	0.3192	0.3192	None
0.4605	5.0	1955	0.1394	0.905	0.3255	0.3255	None
0.4196	6.0	2346	0.1220	0.923	0.3003	0.3003	None
0.3848	7.0	2737	0.1168	0.8987	0.3347	0.3347	None
0.3472	8.0	3128	0.1281	0.872	0.3706	0.3706	None
0.312	9.0	3519	0.1217	0.8127	0.4450	0.4450	None
0.312	10.0	3910	0.1136	0.8007	0.4594	0.4594	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_6_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_6_42