Dog vs Cat Convolutional Neural Network Classifier This project implements a Convolutional Neural Network (CNN) classifier to distinguish between images of dogs and cats. The classifier is trained on a dataset of labeled images and can predict whether an input image contains a dog or a cat.
The training and testing dataset consists of a large collection of labeled images of dogs and cats. Each image is labeled as either a dog or a cat, allowing the model to learn from the patterns and features specific to each class. Since the dataset is large I recommend to download the zip file of the project and run using Jupyter Notebook as working with Google Collaboratory for large dataset is not prefferable.
The CNN classifier is built using deep learning techniques and consists of several convolutional layers, pooling layers, and fully connected layers. The architecture is designed to extract hierarchical features from the input images and make accurate predictions.
The model architecture includes the following components:
Convolutional Layers: These layers apply a set of learnable filters to the input image, capturing local patterns and features. Each filter performs a convolution operation, followed by an activation function to introduce non-linearity.
Pooling Layers: These layers reduce the spatial dimensions of the feature maps, reducing the computational complexity and providing translation invariance. Common pooling techniques include max pooling or average pooling.
Fully Connected Layers: These layers connect every neuron in one layer to every neuron in the subsequent layer. They capture global patterns and relationships in the high-level feature representations obtained from the convolutional layers.
Activation Functions: Non-linear activation functions like ReLU (Rectified Linear Unit) are applied to introduce non-linearity, enabling the model to learn complex relationships in the data.
Dropout: Dropout is used to regularize the model and prevent overfitting. It randomly sets a fraction of the input units to zero during training, forcing the model to learn more robust and generalizable features.
The model is trained using the labeled dataset of dog and cat images. The training process involves the following steps:
Data Preprocessing: The input images are resized to a fixed size and normalized to bring pixel values within a certain range. This ensures consistent input for the model and speeds up the training process.
Model Initialization: The CNN classifier is initialized with random weights and biases.
Forward Propagation: The input images are fed through the model, and the predicted probabilities for each class (dog or cat) are computed using a softmax activation function.
Loss Calculation: The cross-entropy loss is calculated between the predicted probabilities and the true labels. The loss measures the dissimilarity between the predicted and actual distributions.
Backward Propagation: The gradients of the model parameters with respect to the loss are computed using backpropagation. These gradients are then used to update the model weights using an optimization algorithm such as Stochastic Gradient Descent (SGD) or Adam.
Iterative Optimization: The forward and backward propagation steps are repeated for multiple epochs, gradually adjusting the model weights to minimize the loss and improve classification accuracy.
After training, the model's performance is evaluated on a separate test set of images. The trained model predicts the labels for these images, and the accuracy is calculated by comparing the predictions with the ground truth labels.
To use the trained model for inference on new images, follow these steps:
Load the trained model weights and architecture.
Preprocess the input image by resizing it to the same dimensions as the training images and normalizing the pixel values.
Pass the preprocessed image through the model.
Retrieve the predicted probabilities for each class (dog and cat) and choose the class with the highest probability as the predicted label.
By following these steps, you can classify new images as either dogs or cats using the trained CNN classifier.
This project requires the following dependencies:
Python (3.6 or higher)
TensorFlow (2.0 or higher)
Keras (2.0 or higher)
NumPy
Matplotlib
Make sure to install these dependencies before running the code.
Conclusion The Dog vs Cat Convolutional Neural Network Classifier project demonstrates the application of deep learning techniques to distinguish between images of dogs and cats. By training a CNN classifier on a labeled dataset, the model can make accurate predictions on unseen images, enabling automated classification of dogs and cats.