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swin-base-patch4-window7-224-in22k-finetuned-cifar10 – AI Model by Weili | AlphaNeural AI
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swin-base-patch4-window7-224-in22k-finetuned-cifar10
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transformers
pytorch
tensorboard
swin
image-classification
generated_from_trainer
cifar10
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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swin-base-patch4-window7-224-in22k-finetuned-cifar10
This model is a fine-tuned version of
microsoft/swin-base-patch4-window7-224-in22k
on the cifar10 dataset. It achieves the following results on the evaluation set:
Loss: 0.0365
Accuracy: 0.989
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: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3
Training results
Training Loss
Epoch
Step
Validation Loss
Accuracy
0.2666
1.0
390
0.0560
0.9817
0.2169
2.0
780
0.0437
0.9864
0.2105
3.0
1170
0.0365
0.989
Framework versions
Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.7.1
Tokenizers 0.13.2
