Image classification is a fundamental task in computer vision, and building accurate models to classify images is crucial for various applications. In this tutorial, we’ll dive into the world of deep learning and machine learning to create an image classification model using TensorFlow and Keras. By the end of this tutorial, you’ll have a practical understanding of how to build, train, and evaluate a machine learning model for image classification.
Before we begin building the image classification model, let’s set up our Python environment and install the necessary libraries.
1. Install TensorFlow and Keras:
pip install tensorflow keras
2. Importing Required Libraries:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
For this tutorial, we’ll use a widely-used dataset for image classification: the CIFAR-10 dataset. This dataset consists of 60,000 32×32 color images in 10 different classes, with 6,000 images per class.
1. Loading CIFAR-10 Dataset:
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
2. Exploring the Dataset:
# Display a few sample images
plt.figure(figsize=(10, 10))
for i in range(9):
plt.subplot(3, 3, i + 1)
plt.imshow(x_train[i])
plt.title(f"Class: {y_train[i][0]}")
plt.axis("off")
plt.show()
3. Preprocessing the Data:
# Normlize pixel values to be between 0 and 1
x_train, x_test = x_train / 255.0, x_test / 255.0# One-hot encode the labels
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
Be the first to comment