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vit-base-patch16-224-finetuned-cifar10 – AI Model by Weili | AlphaNeural AI
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vit-base-patch16-224-finetuned-cifar10
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transformers
pytorch
tensorboard
vit
image-classification
generated_from_trainer
cifar10
apache-2.0
model-index
autotrain_compatible
endpoints_compatible
us
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vit-base-patch16-224-finetuned-cifar10
This model is a fine-tuned version of
google/vit-base-patch16-224
on the cifar10 dataset. It achieves the following results on the evaluation set:
Loss: 0.0427
Accuracy: 0.9876
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
0.2518
1.0
390
0.0609
0.9821
0.1985
2.0
780
0.0532
0.983
0.197
3.0
1170
0.0427
0.9876
Framework versions
Transformers 4.25.1
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2
