metadata

language: es
tags:
  - sagemaker
  - vit
  - ImageClassification
  - generated_from_trainer
license: apache-2.0
datasets:
  - cifar100
metrics:
  - accuracy
model-index:
  - name: vit_base-224-in21k-ft-cifar100
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: Cifar100
          type: cifar100
        metrics:
          - name: Accuracy,
            type: accuracy,
            value: 0.9148

Model vit_base-224-in21k-ft-cifar100

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

Base model citation

BibTeX entry and citation info

@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

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. This dataset,CIFAR100, is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs).

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 = 0.9148

Model in action

Usage for Image Classification

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-cifar100')
inputs = feature_extractor(images=image, return_tensors="pt")

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

Created by Eduardo Muñoz/@edumunozsala