
## RandAugment for Image Classification for Improved Robustness on the 🤗Hub!

[Paper](https://arxiv.org/abs/1909.13719) | [Keras Tutorial](https://keras.io/examples/vision/randaugment/)

Keras Tutorial Credit goes to : [Sayak Paul](https://twitter.com/RisingSayak)

**Excerpt from the Tutorial:**

Data augmentation is a very useful technique that can help to improve the translational invariance of convolutional neural networks (CNN). RandAugment is a stochastic vision data augmentation routine composed of strong augmentation transforms like color jitters, Gaussian blurs, saturations, etc. along with more traditional augmentation transforms such as random crops.

Recently, it has been a key component of works like [Noisy Student Training](https://arxiv.org/abs/1911.04252) and [Unsupervised Data Augmentation for Consistency Training](https://arxiv.org/abs/1904.12848). It has been also central to the success of EfficientNets.

## About The dataset

The model was trained on [**CIFAR-10**](https://huggingface.co/datasets/cifar10), consisting of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.


{"license": "apache-2.0", "tags": ["RandAugment", "Image Classification"], "datasets": ["cifar10"], "metrics": ["Accuracy"]}
keras RandAugment Image Classification dataset:cifar10 arxiv:1909.13719 arxiv:1911.04252 arxiv:1904.12848 license:apache-2.0 has_space region:us
TAGS
#keras #RandAugment #Image Classification #dataset-cifar10 #arxiv-1909.13719 #arxiv-1911.04252 #arxiv-1904.12848 #license-apache-2.0 #has_space #region-us 


## RandAugment for Image Classification for Improved Robustness on the Hub!

Paper | Keras Tutorial

Keras Tutorial Credit goes to : Sayak Paul

Excerpt from the Tutorial:

Data augmentation is a very useful technique that can help to improve the translational invariance of convolutional neural networks (CNN). RandAugment is a stochastic vision data augmentation routine composed of strong augmentation transforms like color jitters, Gaussian blurs, saturations, etc. along with more traditional augmentation transforms such as random crops.

Recently, it has been a key component of works like Noisy Student Training and Unsupervised Data Augmentation for Consistency Training. It has been also central to the success of EfficientNets.

## About The dataset

The model was trained on CIFAR-10, consisting of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.


## RandAugment for Image Classification for Improved Robustness on the Hub!

Paper | Keras Tutorial

Keras Tutorial Credit goes to : Sayak Paul

Excerpt from the Tutorial:

Data augmentation is a very useful technique that can help to improve the translational invariance of convolutional neural networks (CNN). RandAugment is a stochastic vision data augmentation routine composed of strong augmentation transforms like color jitters, Gaussian blurs, saturations, etc. along with more traditional augmentation transforms such as random crops.

Recently, it has been a key component of works like Noisy Student Training and Unsupervised Data Augmentation for Consistency Training. It has been also central to the success of EfficientNets. ## About The dataset

The model was trained on CIFAR-10, consisting of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
TAGS
#keras #RandAugment #Image Classification #dataset-cifar10 #arxiv-1909.13719 #arxiv-1911.04252 #arxiv-1904.12848 #license-apache-2.0 #has_space #region-us 
 ## RandAugment for Image Classification for Improved Robustness on the Hub!

Paper | Keras Tutorial

Keras Tutorial Credit goes to : Sayak Paul

Excerpt from the Tutorial:

Data augmentation is a very useful technique that can help to improve the translational invariance of convolutional neural networks (CNN). RandAugment is a stochastic vision data augmentation routine composed of strong augmentation transforms like color jitters, Gaussian blurs, saturations, etc. along with more traditional augmentation transforms such as random crops.

Recently, it has been a key component of works like Noisy Student Training and Unsupervised Data Augmentation for Consistency Training. It has been also central to the success of EfficientNets. ## About The dataset

The model was trained on CIFAR-10, consisting of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
passage: TAGS
#keras #RandAugment #Image Classification #dataset-cifar10 #arxiv-1909.13719 #arxiv-1911.04252 #arxiv-1904.12848 #license-apache-2.0 #has_space #region-us 
## RandAugment for Image Classification for Improved Robustness on the Hub!

Paper | Keras Tutorial

Keras Tutorial Credit goes to : Sayak Paul

Excerpt from the Tutorial:

Data augmentation is a very useful technique that can help to improve the translational invariance of convolutional neural networks (CNN). RandAugment is a stochastic vision data augmentation routine composed of strong augmentation transforms like color jitters, Gaussian blurs, saturations, etc. along with more traditional augmentation transforms such as random crops.

Recently, it has been a key component of works like Noisy Student Training and Unsupervised Data Augmentation for Consistency Training. It has been also central to the success of EfficientNets.## About The dataset

The model was trained on CIFAR-10, consisting of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.