You can deploy this model and start earning money today!

jialicheng/
unlearn-cl_cifar10_swin-base_salun_2_42

Name: unlearn-cl_cifar10_swin-base_salun_2_42
Availability: InStock

generated_from_trainer

microsoft/swin-base-patch4-window7-224

finetune

apache-2.0

endpoints_compatible

Views

No views yet

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: 0.0553
Accuracy: 0.9843
Dt Accuracy: 0.9843
Df Accuracy: 0.172
Unlearn Overall Accuracy: 0.9595
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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Overall Accuracy	Unlearn Overall Accuracy	Time
No log	1.0	391	0.0851	0.911	0.3171	0.3171	None
0.2997	2.0	782	0.0704	0.921	0.3032	0.3032	None
0.2818	3.0	1173	0.0715	0.916	0.3103	0.3103	None
0.2716	4.0	1564	0.0783	0.892	0.3437	0.3437	None
0.2716	5.0	1955	0.0686	0.881	0.3587	0.3587	None
0.2555	6.0	2346	0.0696	0.834	0.4195	0.4195	None
0.2379	7.0	2737	0.0620	0.814	0.4442	0.4442	None
0.2186	8.0	3128	0.0713	0.721	0.5483	0.5483	None
0.2064	9.0	3519	0.0644	0.688	0.5821	0.5821	None
0.2064	10.0	3910	0.0633	0.597	0.6674	0.6674	None
0.1912	11.0	4301	0.0632	0.523	0.7294	0.7294	None
0.1747	12.0	4692	0.0605	0.473	0.7681	0.7681	None
0.1629	13.0	5083	0.0630	0.4	0.8207	0.8207	None
0.1629	14.0	5474	0.0661	0.359	0.8473	0.8473	None
0.1485	15.0	5865	0.0574	0.332	0.8659	0.8659	None
0.1364	16.0	6256	0.0544	0.243	0.9203	0.9203	None
0.128	17.0	6647	0.0554	0.255	0.9130	0.9130	None
0.118	18.0	7038	0.0567	0.193	0.9476	0.9476	None
0.118	19.0	7429	0.0559	0.188	0.9509	0.9509	None
0.11	20.0	7820	0.0553	0.172	0.9595	0.9595	None

Framework versions

Transformers 4.48.0
Pytorch 2.2.2+cu118
Datasets 3.2.0
Tokenizers 0.21.0

jialicheng/unlearn-cl_cifar10_swin-base_salun_2_42

42

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jialicheng/
unlearn-cl_cifar10_swin-base_salun_2_42