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# cifar10_outputs

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.
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

{"license": "apache-2.0", "tags": ["image-classification", "vision", "generated_from_trainer"], "datasets": ["cifar10"], "metrics": ["accuracy"], "model-index": [{"name": "cifar10_outputs", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "cifar10", "type": "cifar10", "args": "plain_text"}, "metrics": [{"type": "accuracy", "value": 0.991421568627451, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard optimum_graphcore vit image-classification vision generated_from_trainer dataset:cifar10 license:apache-2.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #optimum_graphcore #vit #image-classification #vision #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #model-index #endpoints_compatible #region-us 




# 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

# 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
TAGS
#transformers #pytorch #tensorboard #optimum_graphcore #vit #image-classification #vision #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
 # 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
passage: TAGS
#transformers #pytorch #tensorboard #optimum_graphcore #vit #image-classification #vision #generated_from_trainer #dataset-cifar10 #license-apache-2.0 #model-index #endpoints_compatible #region-us 
# 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