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vit-base-patch16-224-in21k-finetuned-cifar10

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0503
  • Accuracy: 0.9875

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • 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
0.3118 1.0 1562 0.1135 0.9778
0.2717 2.0 3124 0.0619 0.9867
0.1964 3.0 4686 0.0503 0.9875

Framework versions

  • Transformers 4.18.0.dev0
  • Pytorch 1.11.0
  • Datasets 2.0.0
  • Tokenizers 0.11.6
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Dataset used to train tanlq/vit-base-patch16-224-in21k-finetuned-cifar10

Space using tanlq/vit-base-patch16-224-in21k-finetuned-cifar10

Evaluation results