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# vit-base-clothing-leafs-example

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: 6.1420
- Accuracy: 0.0448

## 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: 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 8.6059        | 0.14  | 1000  | 8.5844          | 0.0002   |
| 8.5506        | 0.28  | 2000  | 8.5189          | 0.0010   |
| 8.4931        | 0.41  | 3000  | 8.4641          | 0.0012   |
| 8.4223        | 0.55  | 4000  | 8.3495          | 0.0016   |
| 8.3144        | 0.69  | 5000  | 8.2552          | 0.0021   |
| 8.1936        | 0.83  | 6000  | 8.1385          | 0.0024   |
| 8.0638        | 0.97  | 7000  | 7.9924          | 0.0028   |
| 7.8485        | 1.11  | 8000  | 7.8366          | 0.0036   |
| 7.6933        | 1.24  | 9000  | 7.6595          | 0.0045   |
| 7.5808        | 1.38  | 10000 | 7.5232          | 0.0062   |
| 7.4352        | 1.52  | 11000 | 7.3816          | 0.0070   |
| 7.3279        | 1.66  | 12000 | 7.2853          | 0.0084   |
| 7.2141        | 1.8   | 13000 | 7.1553          | 0.0105   |
| 7.151         | 1.94  | 14000 | 7.0853          | 0.0119   |
| 6.9695        | 2.07  | 15000 | 7.0088          | 0.0134   |
| 6.8563        | 2.21  | 16000 | 6.9409          | 0.0139   |
| 6.8019        | 2.35  | 17000 | 6.8634          | 0.0158   |
| 6.7372        | 2.49  | 18000 | 6.8001          | 0.0175   |
| 6.6903        | 2.63  | 19000 | 6.7323          | 0.0191   |
| 6.6482        | 2.77  | 20000 | 6.6638          | 0.0207   |
| 6.5669        | 2.9   | 21000 | 6.6090          | 0.0239   |
| 6.4484        | 3.04  | 22000 | 6.5441          | 0.0240   |
| 6.2568        | 3.18  | 23000 | 6.5015          | 0.0273   |
| 6.2452        | 3.32  | 24000 | 6.4589          | 0.0304   |
| 6.2002        | 3.46  | 25000 | 6.4312          | 0.0310   |
| 6.1699        | 3.6   | 26000 | 6.3723          | 0.0319   |
| 6.1284        | 3.73  | 27000 | 6.3324          | 0.0343   |
| 6.1186        | 3.87  | 28000 | 6.3029          | 0.0350   |
| 6.0611        | 4.01  | 29000 | 6.2723          | 0.0381   |
| 5.7883        | 4.15  | 30000 | 6.2527          | 0.0383   |
| 5.7684        | 4.29  | 31000 | 6.2186          | 0.0392   |
| 5.7701        | 4.43  | 32000 | 6.2031          | 0.0403   |
| 5.7473        | 4.56  | 33000 | 6.1777          | 0.0430   |
| 5.735         | 4.7   | 34000 | 6.1634          | 0.0442   |
| 5.7324        | 4.84  | 35000 | 6.1494          | 0.0443   |
| 5.6949        | 4.98  | 36000 | 6.1420          | 0.0448   |


### 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", "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
===============================


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: 6.1420
* Accuracy: 0.0448


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: 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: 5
* 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: 0.0002
* 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: 5
* 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: 0.0002
* 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: 5
* 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