RezaKhosravi72 / Image_Classification_with_CNN

The "Image Classification with Convolutional Neural Networks (CNN)" project is a demonstration of leveraging deep learning, specifically Convolutional Neural Networks, to classify images. In this project, a CNN is trained to distinguish between cats and dogs, showcasing the power of deep learning in computer vision tasks.

Image Classification with Convolutional Neural Networks (CNN)

Overview:

The "Image Classification with Convolutional Neural Networks (CNN)" project is a demonstration of leveraging deep learning, specifically Convolutional Neural Networks, to classify images. In this project, a CNN is trained to distinguish between cats and dogs, showcasing the power of deep learning in computer vision tasks.

Key Components and Steps:

1. Library Imports:

• The project begins with importing essential libraries, including TensorFlow and Keras, which provide powerful tools for deep learning and image processing.

2. Data Preprocessing:

• The training and test data are preprocessed using image augmentation techniques to enhance the model's ability to generalize.
• Training data is augmented with operations like rescaling, shearing, zooming, and horizontal flipping, while the test data is only rescaled for consistent processing.

3. Building the CNN:

• A Convolutional Neural Network is constructed with a sequential architecture, consisting of convolutional layers, pooling layers, flattening, dense layers, and an output layer.
• Convolutional layers capture features in the images, and pooling layers reduce the spatial dimensions.
• The model employs Rectified Linear Unit (ReLU) activation in convolutional layers and sigmoid activation in the output layer for binary classification.

4. Training the CNN:

• The CNN is compiled with the Adam optimizer and binary cross-entropy loss for binary classification.
• Training is performed on the training set, and the model's performance is evaluated on the test set over 25 epochs.

5. Making Predictions:

• A single image is loaded for prediction, and the CNN classifies it as either a cat or a dog.
• The project showcases how the trained CNN can be used for real-world image classification tasks.

Key Insights:

• Convolutional Neural Networks are exceptionally effective in image classification tasks, offering the ability to learn and recognize complex features in images.
• Data preprocessing, including augmentation, is crucial for training a robust model that can generalize well to new data.

Dataset Source:

The project uses a custom dataset of cat and dog images. While not mentioned in the code, this dataset is crucial for training and testing the CNN for cat vs. dog classification.

On the whole, this project is undoubtedly practical as it demonstrates the application of CNNs in image classification, an essential area in computer vision. It highlights the model's ability to learn and classify images, showcasing deep learning's capabilities in real-world applications.