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# platzi-vit-model-Joaquin-Romero

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 datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0613
- Accuracy: 0.9850

## Model description

It's a Image Classification model performed 

## Intended uses & limitations

None

## Training and evaluation data

Beans dataset

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1475        | 3.85  | 500  | 0.0613          | 0.9850   |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["Image-Classification", "generated_from_trainer"], "datasets": ["beans"], "metrics": ["accuracy"], "widget": [{"src": "https://huggingface.co/platzi/platzi-vit-base-beans/resolve/main/healthy.jpeg", "example_title": "Healthy"}, {"src": "https://huggingface.co/platzi/platzi-vit-base-beans/resolve/main/bean_rust.jpeg", "example_title": "Bean Rust"}], "model-index": [{"name": "platzi-vit-model-Joaquin-Romero", "results": []}]}
transformers pytorch tensorboard vit image-classification Image-Classification generated_from_trainer dataset:beans license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #Image-Classification #generated_from_trainer #dataset-beans #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

platzi-vit-model-Joaquin-Romero
===============================


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


* Loss: 0.0613
* Accuracy: 0.9850


Model description
-----------------


It's a Image Classification model performed


Intended uses & limitations
---------------------------


None


Training and evaluation data
----------------------------


Beans dataset


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 8
* 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


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 8
* 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 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #Image-Classification #generated_from_trainer #dataset-beans #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: 8
* 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 ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #Image-Classification #generated_from_trainer #dataset-beans #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: 8
* 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### Training results### Framework versions


* Transformers 4.27.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2