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# vit-base-mnist

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: 0.0236
- Accuracy: 0.9949

## 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: 5.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3717        | 1.0   | 6375  | 0.0522          | 0.9893   |
| 0.3453        | 2.0   | 12750 | 0.0370          | 0.9906   |
| 0.3736        | 3.0   | 19125 | 0.0308          | 0.9916   |
| 0.3224        | 4.0   | 25500 | 0.0269          | 0.9939   |
| 0.2846        | 5.0   | 31875 | 0.0236          | 0.9949   |


### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.11.0a0+17540c5
- 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": "vit-base-mnist", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "mnist", "type": "mnist", "config": "mnist", "split": "train", "args": "mnist"}, "metrics": [{"type": "accuracy", "value": 0.9948888888888889, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification vision generated_from_trainer dataset:mnist license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #vision #generated_from_trainer #dataset-mnist #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

vit-base-mnist
==============


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: 0.0236
* Accuracy: 0.9949


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: 5.0


### Training results



### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.11.0a0+17540c5
* Datasets 2.4.0
* Tokenizers 0.12.1



### 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: 5.0 ### Training results ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.11.0a0+17540c5
* Datasets 2.4.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #vision #generated_from_trainer #dataset-mnist #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### 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: 5.0 ### Training results ### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.11.0a0+17540c5
* Datasets 2.4.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #tensorboard #vit #image-classification #vision #generated_from_trainer #dataset-mnist #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### 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: 5.0### Training results### Framework versions


* Transformers 4.22.0.dev0
* Pytorch 1.11.0a0+17540c5
* Datasets 2.4.0
* Tokenizers 0.12.1