Image Classification Project

In this project, we will build a simple CNN to classify images into categories. Image classification involves predicting the category or label of an input image.

Dataset for Classification

We will use the CIFAR-10 dataset, which consists of 60,000 images in 10 categories such as airplanes, cars, birds, and cats. Each image is 32×32 pixels and has 3 color channels (RGB).

Loading the Dataset

We will load the CIFAR-10 dataset using TensorFlow:

import tensorflow as tf
from tensorflow.keras.datasets import cifar10

# Load dataset
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# Normalize the pixel values to [0, 1]
x_train = x_train / 255.0
x_test = x_test / 255.0

print("Training data shape:", x_train.shape)

Building the CNN Model

We will define a CNN architecture with convolutional, pooling, and fully connected layers:

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# Define the CNN model
model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    MaxPooling2D((2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D((2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

Compiling the Model

We compile the model with:

• Loss Function: Categorical cross-entropy for multi-class classification.
• Optimizer: Adam optimizer for efficient learning.
• Metrics: Accuracy to evaluate performance.

# Compile the model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

Training the Model

We train the model using the training data for 10 epochs. During training, the model learns to classify images into categories.

# Train the model
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

Evaluating the Model

After training, we evaluate the model on the test data to check its accuracy:

# Evaluate the model
loss, accuracy = model.evaluate(x_test, y_test)
print(f"Test Accuracy: {accuracy * 100:.2f}%")

Making Predictions

We can use the trained model to make predictions on new images:

# Predict the category of a test image
import numpy as np

sample_image = x_test[0]
sample_label = y_test[0]

prediction = np.argmax(model.predict(sample_image[np.newaxis, ...]))
print(f"Predicted Label: {prediction}, Actual Label: {sample_label[0]}")

Challenges in Image Classification

Common challenges include:

• Overfitting: Model performs well on training data but poorly on new data.
• Class Imbalance: Unequal representation of categories in the dataset.
• Computational Cost: Training deep networks requires significant resources.

Summary and Next Steps

In this project, we:

• Built and trained a CNN for image classification using the CIFAR-10 dataset.
• Learned to load, preprocess, and evaluate the dataset.
• Discussed challenges in image classification tasks.

Next, we’ll explore data augmentation techniques to improve model performance by generating variations of training images.