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cifar10_outputs

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: 0.0806
  • Accuracy: 0.9914

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: 0.0001
  • train_batch_size: 17
  • eval_batch_size: 17
  • seed: 1337
  • distributed_type: IPU
  • gradient_accumulation_steps: 128
  • total_train_batch_size: 8704
  • total_eval_batch_size: 272
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.25
  • num_epochs: 100.0
  • training precision: Mixed Precision

Training results

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.10.0+cpu
  • Datasets 2.3.3.dev0
  • Tokenizers 0.12.1
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Dataset used to train jimypbr/cifar10_outputs

Evaluation results