vit-base-patch16-224-in21k-image-classification-sagemaker

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

• Loss: 0.3033
• Accuracy: 0.972

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	313	1.4603	0.936
1.6548	2.0	626	0.4451	0.966
1.6548	3.0	939	0.3033	0.972

Framework versions

• Transformers 4.6.1
• Pytorch 1.7.1
• Datasets 1.6.2
• Tokenizers 0.10.3