---
language: es
tags:
- sagemaker
- vit
- ImageClassification
- generated_from_trainer
license: apache-2.0
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: vit_base-224-in21k-ft-cifar10
  results:
  - task:
        name: Image Classification
        type: image-classification
    dataset:
        name: "Cifar10" 
        type: cifar10
    metrics:
       - name: Accuracy
         type: accuracy
         value: 0.97
---

# Model vit_base-224-in21k-ft-cifar10

## **A finetuned model for Image classification in Spanish**

This model was trained using Amazon SageMaker and the Hugging Face Deep Learning container,
The base model is **Vision Transformer (base-sized model)** which  is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels.[Link to base model](https://huggingface.co/google/vit-base-patch16-224-in21k) 

## Base model citation
### BibTeX entry and citation info

```bibtex
@misc{wu2020visual,
      title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, 
      author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda},
      year={2020},
      eprint={2006.03677},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

## Dataset
[Link to dataset description](http://www.cs.toronto.edu/~kriz/cifar.html)

The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton


The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.


The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.


Sizes of datasets:
- Train dataset: 50,000
- Test dataset: 10,000


## Intended uses & limitations

This model is intented for Image Classification.


## Hyperparameters
    {
    "epochs": "5",
    "train_batch_size": "32",    
    "eval_batch_size": "8",
    "fp16": "true",
    "learning_rate": "1e-05",
    }

## Test results

- Accuracy = 0.97


## Model in action

### Usage for Image Classification

```python
from transformers import ViTFeatureExtractor, ViTModel
from PIL import Image
import requests

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
model = ViTModel.from_pretrained('edumunozsala/vit_base-224-in21k-ft-cifar10')
inputs = feature_extractor(images=image, return_tensors="pt")

outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```

Created by [Eduardo Muñoz/@edumunozsala](https://github.com/edumunozsala)