You can deploy this model and start earning money today!

jialicheng/
unlearn-cl_cifar10_swin-base_random_label_2_42

Name: unlearn-cl_cifar10_swin-base_random_label_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: 0.0496
Accuracy: 0.986
Dt Accuracy: 0.986
Df Accuracy: 0.038
Unlearn Overall Accuracy: 0
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: 5e-05
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	0.0565	0.987	0.2030	0.2030	None
0.2365	2.0	782	0.0534	0.993	0.1932	0.1932	None
0.2213	3.0	1173	0.0561	0.981	0.2126	0.2126	None
0.2179	4.0	1564	0.0667	0.95	0.2607	0.2607	None
0.2179	5.0	1955	0.0634	0.889	0.3481	0.3481	None
0.207	6.0	2346	0.0590	0.797	0.4644	0.4644	None
0.192	7.0	2737	0.0592	0.673	0.5975	0.5975	None
0.1782	8.0	3128	0.0655	0.56	0.6992	0.6992	None
0.1707	9.0	3519	0.0536	0.528	0.7264	0.7264	None
0.1707	10.0	3910	0.0609	0.401	0.8198	0.8198	None
0.1597	11.0	4301	0.0595	0.298	0.8872	0.8872	None
0.147	12.0	4692	0.0623	0.199	0.9441	0.9441	None
0.1408	13.0	5083	0.0585	0.192	0.9484	0.9484	None
0.1408	14.0	5474	0.0529	0.154	0.9694	0.9694	None
0.1312	15.0	5865	0.0538	0.145	0.9739	0.9739	None
0.1237	16.0	6256	0.0524	0.08	0	0	None
0.1148	17.0	6647	0.0499	0.07	0	0	None
0.1101	18.0	7038	0.0508	0.053	0	0	None
0.1101	19.0	7429	0.0495	0.051	0	0	None
0.1061	20.0	7820	0.0496	0.038	0	0	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_random_label_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_random_label_2_42