Python Image Classification using Keras in Tensorflow Backend

Image classification is a popular task in the field of computer vision. It involves training a model to correctly identify the category of an image from a set of predefined categories. In this article, we will use Python, Keras, and the Tensorflow backend to build an image classification model.

Step 1: Install Keras and Tensorflow

Before we begin, make sure you have Keras and Tensorflow installed. You can install them using the following commands:


pip install keras
pip install tensorflow


Step 2: Preprocess the Data

The first step in building an image classification model is to preprocess the data. This involves loading the images, resizing them to a consistent size, and normalizing the pixel values.

Step 3: Build the Model

Next, we will build the image classification model using Keras. We can use a pre-trained model such as VGG16 or ResNet, or we can build our own model from scratch using convolutional neural networks (CNNs).

Step 4: Train the Model

Once the model is built, we can train it on our dataset using the fit() function in Keras. During training, the model learns to make predictions by adjusting its parameters based on the input data and the true labels.

Step 5: Evaluate the Model

After training, we can evaluate the performance of the model using the test dataset. We can measure the accuracy, precision, recall, and other metrics to assess how well the model is performing.

Step 6: Make Predictions

Finally, we can use the trained model to make predictions on new, unseen images. We can use the predict() function in Keras to get the model’s predictions for a set of images.

Conclusion

In this article, we have demonstrated how to build an image classification model using Python, Keras, and the Tensorflow backend. By following these steps, you can create and train your own image classification models for a variety of applications.