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# vit-mae-large-ai-or-not

This model is a fine-tuned version of [facebook/vit-mae-large](https://huggingface.co/facebook/vit-mae-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1883
- Accuracy: 0.9683

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3623        | 0.19  | 200  | 0.2099          | 0.9243   |
| 0.2465        | 0.38  | 400  | 0.4055          | 0.8545   |
| 0.2164        | 0.57  | 600  | 0.1808          | 0.9259   |
| 0.1943        | 0.76  | 800  | 0.1765          | 0.9329   |
| 0.1723        | 0.95  | 1000 | 0.2083          | 0.9313   |
| 0.118         | 1.15  | 1200 | 0.2295          | 0.9168   |
| 0.0812        | 1.34  | 1400 | 0.1600          | 0.9511   |
| 0.082         | 1.53  | 1600 | 0.1331          | 0.9624   |
| 0.0863        | 1.72  | 1800 | 0.1352          | 0.9511   |
| 0.0858        | 1.91  | 2000 | 0.1643          | 0.9506   |
| 0.056         | 2.1   | 2200 | 0.1930          | 0.9586   |
| 0.0319        | 2.29  | 2400 | 0.1595          | 0.9624   |
| 0.0206        | 2.48  | 2600 | 0.2937          | 0.9447   |
| 0.0299        | 2.67  | 2800 | 0.1680          | 0.9603   |
| 0.0213        | 2.86  | 3000 | 0.1746          | 0.9586   |
| 0.0164        | 3.05  | 3200 | 0.1579          | 0.9624   |
| 0.0019        | 3.24  | 3400 | 0.1787          | 0.9646   |
| 0.0022        | 3.44  | 3600 | 0.1976          | 0.9640   |
| 0.0023        | 3.63  | 3800 | 0.2017          | 0.9651   |
| 0.0045        | 3.82  | 4000 | 0.1883          | 0.9683   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "vit-mae-large-ai-or-not", "results": []}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

vit-mae-large-ai-or-not
=======================


This model is a fine-tuned version of facebook/vit-mae-large on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1883
* Accuracy: 0.9683


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: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 16
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.9.0
* Tokenizers 0.13.2