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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

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KwokHing/TF2-Cifar10-CNN-Demo

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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

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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

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