
## Image Classification using MobileViT
This repo contains the model and the notebook [to this Keras example on MobileViT](https://keras.io/examples/vision/mobilevit/).

Full credits to: [Sayak Paul](https://twitter.com/RisingSayak)

## Background Information 
MobileViT architecture (Mehta et al.), combines the benefits of Transformers (Vaswani et al.) and convolutions. With Transformers, we can capture long-range dependencies that result in global representations. With convolutions, we can capture spatial relationships that model locality.

Besides combining the properties of Transformers and convolutions, the authors introduce MobileViT as a general-purpose mobile-friendly backbone for different image recognition tasks. Their findings suggest that, performance-wise, MobileViT is better than other models with the same or higher complexity (MobileNetV3, for example), while being efficient on mobile devices.  

## Training Data
The model is trained on a [tf_flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers)
{"license": ["cc0-1.0"], "library_name": "keras", "tags": ["computer-vision", "image-classification"]}
keras computer-vision image-classification license:cc0-1.0 has_space region:us
TAGS
#keras #computer-vision #image-classification #license-cc0-1.0 #has_space #region-us 


## Image Classification using MobileViT
This repo contains the model and the notebook to this Keras example on MobileViT.

Full credits to: Sayak Paul

## Background Information 
MobileViT architecture (Mehta et al.), combines the benefits of Transformers (Vaswani et al.) and convolutions. With Transformers, we can capture long-range dependencies that result in global representations. With convolutions, we can capture spatial relationships that model locality.

Besides combining the properties of Transformers and convolutions, the authors introduce MobileViT as a general-purpose mobile-friendly backbone for different image recognition tasks. Their findings suggest that, performance-wise, MobileViT is better than other models with the same or higher complexity (MobileNetV3, for example), while being efficient on mobile devices.  

## Training Data
The model is trained on a tf_flowers dataset
## Image Classification using MobileViT
This repo contains the model and the notebook to this Keras example on MobileViT.

Full credits to: Sayak Paul ## Background Information 
MobileViT architecture (Mehta et al.), combines the benefits of Transformers (Vaswani et al.) and convolutions. With Transformers, we can capture long-range dependencies that result in global representations. With convolutions, we can capture spatial relationships that model locality.

Besides combining the properties of Transformers and convolutions, the authors introduce MobileViT as a general-purpose mobile-friendly backbone for different image recognition tasks. Their findings suggest that, performance-wise, MobileViT is better than other models with the same or higher complexity (MobileNetV3, for example), while being efficient on mobile devices. ## Training Data
The model is trained on a tf_flowers dataset
TAGS
#keras #computer-vision #image-classification #license-cc0-1.0 #has_space #region-us 
 ## Image Classification using MobileViT
This repo contains the model and the notebook to this Keras example on MobileViT.

Full credits to: Sayak Paul ## Background Information 
MobileViT architecture (Mehta et al.), combines the benefits of Transformers (Vaswani et al.) and convolutions. With Transformers, we can capture long-range dependencies that result in global representations. With convolutions, we can capture spatial relationships that model locality.

Besides combining the properties of Transformers and convolutions, the authors introduce MobileViT as a general-purpose mobile-friendly backbone for different image recognition tasks. Their findings suggest that, performance-wise, MobileViT is better than other models with the same or higher complexity (MobileNetV3, for example), while being efficient on mobile devices. ## Training Data
The model is trained on a tf_flowers dataset
passage: TAGS
#keras #computer-vision #image-classification #license-cc0-1.0 #has_space #region-us 
## Image Classification using MobileViT
This repo contains the model and the notebook to this Keras example on MobileViT.

Full credits to: Sayak Paul## Background Information 
MobileViT architecture (Mehta et al.), combines the benefits of Transformers (Vaswani et al.) and convolutions. With Transformers, we can capture long-range dependencies that result in global representations. With convolutions, we can capture spatial relationships that model locality.

Besides combining the properties of Transformers and convolutions, the authors introduce MobileViT as a general-purpose mobile-friendly backbone for different image recognition tasks. Their findings suggest that, performance-wise, MobileViT is better than other models with the same or higher complexity (MobileNetV3, for example), while being efficient on mobile devices.## Training Data
The model is trained on a tf_flowers dataset