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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](https://huggingface.co/google/vit-base-patch16-224-in21k) on the cifar10 dataset.

## 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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.10.1
- Datasets 2.1.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["cifar10"], "model-index": [{"name": "vit-base-patch16-224-in21k-finetuned-cifar10", "results": []}]}
transformers pytorch vit image-classification generated_from_trainer dataset:cifar10 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# 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.

## 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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.10.1
- Datasets 2.1.0
- Tokenizers 0.12.1

# 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. ## 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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.10.1
- Datasets 2.1.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # 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. ## 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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.10.1
- Datasets 2.1.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# 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.## 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: 8
- eval_batch_size: 8
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.10.1
- Datasets 2.1.0
- Tokenizers 0.12.1