Image classification using CNN (CIFAR10 dataset) | Deep Learning Tutorial 24 (Tensorflow & Python)

codebasics · Beginner ·📐 ML Fundamentals ·5y ago
In this video we will do small image classification using CIFAR10 dataset in tensorflow. We will use convolutional neural network for this image classification problem. First we will train a model using simple artificial neural network and then check how the performance looks like and then we will train a CNN and see how the model accuracy improves. This tutorial will help you understand why CNN is preferred over ANN for image classification. Code: https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/master/16_cnn_cifar10_small_image_classification/cnn_cifar10_dataset.ipynb Exe…
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⚡ AI Lesson Summary ✦ V3 skills 🛠 Hands-on

This video teaches how to use convolutional neural networks for image classification on the CIFAR10 dataset using TensorFlow and Python, and compares the performance of a simple artificial neural network and a convolutional neural network.

Key Takeaways
  1. Load CIFAR10 dataset into Jupyter notebook
  2. Reshape y_train array from 2D to 1D
  3. Plot image samples using matplotlib
  4. Build a simple artificial neural network with 2 deep layers and 10 categories
  5. Add a convolutional layer with 32 filters of 3x3 size
  6. Add a max pooling layer after convolutional layer
  7. Compile model with Adam optimizer and categorical cross entropy
  8. Train model for 10 epochs
  9. Test model on test set
💡 Using a convolutional neural network with max pooling can significantly improve the accuracy of image classification models compared to simple artificial neural networks.

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