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# aiornot_eman

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0863
- Accuracy: 0.9726

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1235        | 1.0   | 248  | 0.1117          | 0.9547   |
| 0.0512        | 2.0   | 497  | 0.0866          | 0.9690   |
| 0.0175        | 2.99  | 744  | 0.0863          | 0.9726   |


### Framework versions

- Transformers 4.27.3
- Pytorch 1.10.2
- Datasets 2.10.1
- Tokenizers 0.13.2
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["generated_from_trainer"], "datasets": ["competitions/aiornot"], "metrics": ["accuracy"], "pipeline_tag": "image-classification", "model-index": [{"name": "aiornot_eman", "results": []}]}
transformers pytorch vit image-classification generated_from_trainer en dataset:competitions/aiornot license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #en #dataset-competitions/aiornot #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

aiornot\_eman
=============


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0863
* Accuracy: 0.9726


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: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.27.3
* Pytorch 1.10.2
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.10.2
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #en #dataset-competitions/aiornot #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.27.3
* Pytorch 1.10.2
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #vit #image-classification #generated_from_trainer #en #dataset-competitions/aiornot #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.27.3
* Pytorch 1.10.2
* Datasets 2.10.1
* Tokenizers 0.13.2