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

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: 0.9880
- Accuracy: 0.7166

## 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: 2.5e-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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 2.0202        | 0.14  | 1000  | 1.4969          | 0.6338   |
| 1.3694        | 0.28  | 2000  | 1.2786          | 0.6647   |
| 1.2063        | 0.42  | 3000  | 1.1788          | 0.6794   |
| 1.1544        | 0.56  | 4000  | 1.1320          | 0.6856   |
| 1.1089        | 0.7   | 5000  | 1.1021          | 0.6867   |
| 1.0681        | 0.84  | 6000  | 1.0775          | 0.6935   |
| 1.0483        | 0.98  | 7000  | 1.0461          | 0.7006   |
| 0.9591        | 1.12  | 8000  | 1.0398          | 0.7022   |
| 0.9541        | 1.26  | 9000  | 1.0423          | 0.6981   |
| 0.9382        | 1.4   | 10000 | 1.0322          | 0.7014   |
| 0.9363        | 1.54  | 11000 | 1.0301          | 0.7020   |
| 0.9199        | 1.68  | 12000 | 1.0079          | 0.7106   |
| 0.919         | 1.82  | 13000 | 0.9972          | 0.7120   |
| 0.9203        | 1.96  | 14000 | 1.0011          | 0.7096   |
| 0.8377        | 2.1   | 15000 | 0.9912          | 0.7146   |
| 0.8148        | 2.24  | 16000 | 0.9991          | 0.7121   |
| 0.8153        | 2.38  | 17000 | 1.0070          | 0.7102   |
| 0.8004        | 2.52  | 18000 | 0.9979          | 0.7154   |
| 0.7937        | 2.66  | 19000 | 1.0022          | 0.7136   |
| 0.7989        | 2.8   | 20000 | 0.9880          | 0.7166   |
| 0.7953        | 2.94  | 21000 | 0.9907          | 0.7175   |
| 0.7576        | 3.08  | 22000 | 1.0013          | 0.7136   |
| 0.7018        | 3.22  | 23000 | 1.0022          | 0.7156   |
| 0.7127        | 3.36  | 24000 | 1.0080          | 0.7151   |
| 0.6989        | 3.5   | 25000 | 1.0025          | 0.7159   |
| 0.702         | 3.64  | 26000 | 1.0087          | 0.7167   |
| 0.7122        | 3.78  | 27000 | 1.0042          | 0.7159   |
| 0.6986        | 3.92  | 28000 | 1.0017          | 0.7164   |


### 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_highres", "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\_highres
====================================================


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: 0.9880
* Accuracy: 0.7166


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: 2.5e-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: 2.5e-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: 2.5e-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