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vit-base-patch16-224-finetuned-og-dataset-10e – AI Model by Gokulapriyan | AlphaNeural AI | AlphaNeural AI
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Gokulapriyan
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vit-base-patch16-224-finetuned-og-dataset-10e
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transformers
pytorch
tensorboard
vit
image-classification
generated_from_trainer
imagefolder
apache-2.0
autotrain_compatible
endpoints_compatible
us
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vit-base-patch16-224-finetuned-og-dataset-10e
This model is a fine-tuned version of
google/vit-base-patch16-224
on the imagefolder dataset. It achieves the following results on the evaluation set:
eval_loss: 0.0619
eval_accuracy: 0.9770
eval_runtime: 263.7551
eval_samples_per_second: 46.82
eval_steps_per_second: 0.978
epoch: 6.0
step: 2184
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: 48
eval_batch_size: 48
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 192
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10
Framework versions
Transformers 4.26.1
Pytorch 1.13.1+cu116
Datasets 2.9.0
Tokenizers 0.13.2
