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Image classification is a popular task in computer vision, and multi-class image classification involves assigning a label to an image from a set of predefined classes. In this tutorial, we will walk through the process of building a multi-class image classification model using Python and popular libraries like TensorFlow and Keras.
Make sure you have the following installed:
You can install these libraries using the following command:
For this tutorial, we will use the famous CIFAR-10 dataset, which consists of 60,000 32x32 color images in 10 different classes. You can load the dataset directly using Keras:
Before training the model, it's essential to preprocess the data. Normalize the pixel values to be between 0 and 1, and one-hot encode the labels:
Now, let's build a simple convolutional neural network (CNN) for image classification:
Compile the model with an optimizer, loss function, and metric, then train it using the training data:
Evaluate the model on the test data and print the accuracy:
Congratulations! You have successfully built a multi-class image classification model using Python, TensorFlow, and Keras. Feel free to experiment with different architectures, hyperparameters, and datasets to improve the performance of your model. Image classification is a vast field, and there are many advanced techniques and pre-trained models you can explore for more complex tasks.
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