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# mnist-digit-classification-2022-09-04

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 mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0319
- Accuracy: 0.9923

## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["image-classification", "vision", "generated_from_trainer"], "datasets": ["mnist"], "metrics": ["accuracy"], "model-index": [{"name": "mnist-digit-classification-2022-09-04", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "mnist", "type": "mnist", "config": "mnist", "split": "train", "args": "mnist"}, "metrics": [{"type": "accuracy", "value": 0.9923333333333333, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification vision generated_from_trainer dataset:mnist license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #vit #image-classification #vision #generated_from_trainer #dataset-mnist #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 




# mnist-digit-classification-2022-09-04

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0319
- Accuracy: 0.9923

## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results



### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1

# mnist-digit-classification-2022-09-04

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0319
- Accuracy: 0.9923 ## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0 ### Training results ### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #vit #image-classification #vision #generated_from_trainer #dataset-mnist #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # mnist-digit-classification-2022-09-04

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0319
- Accuracy: 0.9923 ## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0 ### Training results ### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #vit #image-classification #vision #generated_from_trainer #dataset-mnist #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# mnist-digit-classification-2022-09-04

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0319
- Accuracy: 0.9923## 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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0### Training results### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1