---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
model-index:
- name: vit-base-patch16-224-franciscoflores-classification
  results: []
metrics:
- accuracy
language:
- en
- es
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-patch16-224-franciscoflores-classification

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 julien-c/hotdog-not-hotdog dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0074
- Accuracy :0.9966

## Model description

Modelo de clasificacion binaria de imagenes proveniente del modelo 
'google/vit-base-patch16-224-in21k', este modelo se encarga de clasificacion de imagenes de comida en 
donde indica cuales imagenes son comida y cuales no ///////This is a binary image classification model based on
the 'google/vit-base-patch16-224-in21k' model. It is designed for classifying images of food, 
indicating which images depict food and which ones do not

## Intended uses & limitations

More information needed

## Training and evaluation data

el dataset utilizado para su entrenamiento es "sasha/dog-food"
The dataset used for its training is "sasha/dog-food."

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.045         | 1.9   | 500  | 0.0828          |
| 0.0081        | 3.8   | 1000 | 0.0074          |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3