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# vit-base-clothing-leafs-example-full-simple

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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9954
- Accuracy: 0.7155

## 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: 3e-05
- train_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.9495        | 0.14  | 1000  | 1.4553          | 0.6307   |
| 1.3079        | 0.28  | 2000  | 1.2347          | 0.6677   |
| 1.178         | 0.41  | 3000  | 1.1607          | 0.6758   |
| 1.1324        | 0.55  | 4000  | 1.1307          | 0.6824   |
| 1.0928        | 0.69  | 5000  | 1.0956          | 0.6909   |
| 1.0679        | 0.83  | 6000  | 1.0790          | 0.6912   |
| 1.0488        | 0.97  | 7000  | 1.0486          | 0.7014   |
| 0.9548        | 1.11  | 8000  | 1.0449          | 0.7016   |
| 0.9352        | 1.24  | 9000  | 1.0348          | 0.7042   |
| 0.9164        | 1.38  | 10000 | 1.0340          | 0.7034   |
| 0.9267        | 1.52  | 11000 | 1.0178          | 0.7089   |
| 0.9058        | 1.66  | 12000 | 1.0160          | 0.7063   |
| 0.9028        | 1.8   | 13000 | 1.0084          | 0.7111   |
| 0.9093        | 1.94  | 14000 | 1.0009          | 0.7136   |
| 0.8346        | 2.07  | 15000 | 1.0152          | 0.7117   |
| 0.7897        | 2.21  | 16000 | 1.0072          | 0.7141   |
| 0.7869        | 2.35  | 17000 | 1.0088          | 0.7083   |
| 0.7853        | 2.49  | 18000 | 0.9981          | 0.7162   |
| 0.7732        | 2.63  | 19000 | 1.0030          | 0.7149   |
| 0.779         | 2.77  | 20000 | 0.9954          | 0.7155   |
| 0.7655        | 2.9   | 21000 | 0.9972          | 0.7179   |
| 0.74          | 3.04  | 22000 | 1.0114          | 0.7138   |
| 0.6824        | 3.18  | 23000 | 1.0171          | 0.7130   |
| 0.68          | 3.32  | 24000 | 1.0111          | 0.7178   |
| 0.6787        | 3.46  | 25000 | 1.0124          | 0.7151   |
| 0.6808        | 3.6   | 26000 | 1.0181          | 0.7150   |
| 0.6561        | 3.73  | 27000 | 1.0144          | 0.7168   |
| 0.6611        | 3.87  | 28000 | 1.0154          | 0.7155   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-clothing-leafs-example-full-simple", "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-base-clothing-leafs-example-full-simple
===========================================


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


* Loss: 0.9954
* Accuracy: 0.7155


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: 3e-05
* train\_batch\_size: 32
* 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 4.29.2
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 3e-05
* train\_batch\_size: 32
* 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 4.29.2
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3
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: 3e-05
* train\_batch\_size: 32
* 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 4.29.2
* Pytorch 2.0.1
* Datasets 2.12.0
* Tokenizers 0.13.3