https://github.com/kwokhing/tf2-cifar10-cnn-demo

Demo on performing multiclass image classification using Convolutional Neural Network (CNN) in Tensorflow 2. Techniques such as earlystopping, batchnormalizing and dropout are explored to prevent overfitting
https://github.com/kwokhing/tf2-cifar10-cnn-demo

batchnormalization cifar10 cnn-classification convolutional-layers convolutional-neural-networks dropout earlystopping savedmodel tensorboard tensorflow tensorflow2 vgg

Last synced: about 2 months ago
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Demo on performing multiclass image classification using Convolutional Neural Network (CNN) in Tensorflow 2. Techniques such as earlystopping, batchnormalizing and dropout are explored to prevent overfitting

• Host: GitHub
• URL: https://github.com/kwokhing/tf2-cifar10-cnn-demo
• Owner: KwokHing
• Created: 2021-07-25T04:37:30.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-07-25T05:47:31.000Z (over 3 years ago)
• Last Synced: 2023-08-19T08:42:09.977Z (over 1 year ago)
• Topics: batchnormalization, cifar10, cnn-classification, convolutional-layers, convolutional-neural-networks, dropout, earlystopping, savedmodel, tensorboard, tensorflow, tensorflow2, vgg
• Language: Jupyter Notebook
• Homepage:
• Size: 4.12 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Tensorflow 2 CNN Cifar10 Classification

This demo deploys the use of Convolutional Neural Networks (CNN) in Tensorflow 2 to classify Cifar10 images.

- Tensorflow Data Pipeline

- Convolutional Neural Networks (CNN)

- Techniques that helps prevents overfitting (EarlyStopping, BatchNormalization, Dropout)

- Tensorboard

- Saving Model (.h5, tf, weights)

## Getting started

Open `TF2_Cifar10_CNN.ipynb` on a jupyter notebook environment, or Google colab. The notebook consists of further technical details.

## Future Improvements

- Explore the use of data augmentation in image classifcation

- Explore the model performance on Cifar100 dataset