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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