Vit Base 224 In21k Ft Cifar10

🚀 Model vit_base-224-in21k-ft-cifar10

A finetuned model for Image classification in Spanish, leveraging Amazon SageMaker and Hugging Face's Deep Learning container.

🚀 Quick Start

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

✨ Features

• Trained with SageMaker: Utilizes Amazon SageMaker for efficient training.
• Based on Vision Transformer: Leverages the power of the Vision Transformer architecture.
• High Accuracy: Achieves an accuracy of 0.97 on the CIFAR - 10 dataset.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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

📚 Documentation

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.

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 intended 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

🔧 Technical Details

The base model is a Vision Transformer (base - sized model) pretrained on ImageNet - 21k. The fine - tuning process was carried out using Amazon SageMaker and the Hugging Face Deep Learning container.

📄 License

This model is licensed under the apache - 2.0 license.

Model Information

Created by Eduardo Muñoz/@edumunozsala