How to leverage KerasCV for image classification

Image classification is a common task in machine learning, where the goal is to categorize an image into one of several predefined classes. KerasCV is a powerful library that can be used to simplify the process of building and training image classification models. In this article, we will walk through the steps of using KerasCV for image classification.

Step 1: Install KerasCV

The first step is to install KerasCV. You can do this by running the following command:

pip install kerascv

Step 2: Prepare your data

Next, you will need to prepare your data for training. This may involve downloading a dataset, preprocessing the images, and splitting the data into training and validation sets.

Step 3: Build your model

Now it’s time to build your image classification model using KerasCV. You can start by importing the necessary modules:

from kerascv.models import Classifiers

Then, you can choose a pre-trained model architecture from the available options in KerasCV:

model = Classifiers.ResNet18(input_shape=(224, 224, 3), num_classes=10)

Step 4: Train your model

After building your model, you can train it using your prepared data. You can do this by calling the fit method on your model:

model.fit(X_train, y_train, validation_data=(X_val, y_val), batch_size=32, epochs=10)

Step 5: Evaluate your model

Finally, you can evaluate the performance of your model on a test set using the evaluate method:

loss, accuracy = model.evaluate(X_test, y_test)

By following these steps, you can leverage KerasCV to build and train image classification models with ease. Happy coding!