https://github.com/nihadguluzade/convolutional-neural-networks

Implemented CNN for image classification.
https://github.com/nihadguluzade/convolutional-neural-networks

cnn-classification cnn-keras resnet-50 vgg16

Last synced: 6 days ago
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Implemented CNN for image classification.

• Host: GitHub
• URL: https://github.com/nihadguluzade/convolutional-neural-networks
• Owner: nihadguluzade
• Created: 2021-01-13T19:28:51.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2021-01-13T19:29:15.000Z (almost 4 years ago)
• Last Synced: 2023-11-10T18:41:12.540Z (about 1 year ago)
• Topics: cnn-classification, cnn-keras, resnet-50, vgg16
• Language: Jupyter Notebook
• Homepage:
• Size: 5.86 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 
Convolutional Neural Networks




[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CiR8Q7q09nBIUmn5WHml9hW7y1Te5giH?usp=sharing)



In the deep learning and computer vision world, CNNs are getting more and more popular due to their high-performance

results. Thanks to this, it became very straight-forward to create and build convolutional models using different

frameworks such as Keras or TensorFlow. This implementation has been done with Keras.

The model has 3 convolutional layers with dropouts, flatten output of convolutions, and final dense output layer.

The dataset used for this study is CINIC-10, an augmented extension of CIFAR-10 which contains 270,000 images.

See more about the dataset: https://www.kaggle.com/mengcius/cinic10

Additionally, this notebook has VGG-16 and ResNet-50 models with the last 4 layers trainable. As these models require

the input shape to be 224x224x3, the images have to be resized before feeding to the models.