🐾 Cat vs Dog Image Classification

This repository implements a binary image classification model to distinguish between images of cats and dogs using a Convolutional Neural Network (CNN). The project leverages TensorFlow and Keras for building and training the model.

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✨ Features

• Binary classification of cats and dogs using CNN.
• Data normalization and augmentation to improve model robustness.
• Lightweight architecture designed for efficiency and scalability.
• Achieves high accuracy on unseen test data.
• Includes training and testing pipelines with TensorFlow/Keras.

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🧠 Model Architecture

The CNN model used in this project comprises the following layers:

1. Input Layer:

   • Input shape: (256, 256, 3) for RGB images.
   • Images are normalized to the range [0, 1].

2. Convolutional Layers:

   • Three convolutional layers with ReLU activation and 3x3 kernels.
   • Filters: 32, 64, and 128, respectively.
   • Batch Normalization after each convolutional layer.

3. Pooling Layers:

   • MaxPooling layers with 2x2 pool size to reduce spatial dimensions.

4. Fully Connected Layers:

   • Flattened the feature maps into a 1D vector.
   • Two dense layers with 128 and 64 neurons, each with ReLU activation.
   • Dropout layers to prevent overfitting.

5. Output Layer:

   • Dense layer with 1 neuron and a sigmoid activation for binary classification.

🛠 Implementation

Data Preparation

The data is organized into train and test directories with labeled subfolders. Images are resized to 256x256 pixels, and the pixel values are normalized to the range [0, 1].

train_dataset = keras.utils.image_dataset_from_directory(
    directory='/content/train',
    labels="inferred",
    label_mode="int",
    batch_size=32,
    image_size=(256, 256)
)

test_dataset = keras.utils.image_dataset_from_directory(
    directory='/content/test',
    labels="inferred",
    label_mode="int",
    batch_size=32,
    image_size=(256, 256)
)

# Normalization function
def normal(image, label):
    image = tf.cast(image / 255.0, tf.float32)
    return image, label

train_dataset = train_dataset.map(normal)
test_dataset = test_dataset.map(normal)