Computer Vision (Part B)

In the context of computer vision and machine learning, a neural network refers to a computational model inspired by the structure and functioning of the human brain's neural network. A neural network is composed of interconnected nodes called neurons, organized in layers. Each neuron receives inputs, performs a computation, and produces an output that is passed to other neurons. The connections between neurons are associated with weights, which determine the strength of the signal being transmitted. By adjusting these weights during training, a neural network can learn to recognize patterns and make accurate predictions.

The structure of a neural network typically consists of an input layer, one or more hidden layers, and an output layer. The hidden layers, often containing multiple nodes, enable the network to learn complex representations and hierarchies of features. The output layer provides the final predictions or outputs of the network.

Neural networks are trained using a process called backpropagation, which involves feeding input data into the network, comparing the predicted outputs with the actual outputs, and adjusting the weights to minimize the difference between them. This optimization process aims to find the optimal set of weights that allows the network to make accurate predictions on unseen data.

Computer vision neural network

A computer vision neural network refers to a specific type of neural network designed and optimized for tasks related to computer vision, such as image classification, object detection, and image segmentation. These networks are tailored to efficiently process visual data and extract meaningful features and representations.

CNNs are the most used neural networks in computer vision tasks. CNNs are specifically designed to automatically learn hierarchical representations of visual data by applying convolutional and pooling operations. These operations enable the network to capture local patterns and spatial relationships in images, making them highly effective in handling complex visual tasks.

Computer vision neural networks typically consist of multiple layers:

• Convolutional layers perform feature extraction by convolving learned filters with the input image, capturing different image features at different levels of abstraction.
• Pooling layers downsample the extracted features, reducing the dimensionality and retaining the most salient information.
• Fully connected layers combine the extracted features and perform classification or regression tasks.
• Output layer produces the final predictions.

Training a computer vision neural network involves providing it with a labelled dataset and optimizing the network’s weights through backpropagation. During training, the network learns to recognize patterns and make accurate predictions by adjusting the weights based on the comparison between predicted outputs and ground truth labels.

Computer vision neural networks have achieved remarkable performance in various tasks, including image recognition, object detection, and image generation. They have been applied in diverse domains such as autonomous driving, medical imaging, surveillance, and augmented reality. Their ability to automatically learn and extract relevant features from visual data has revolutionized the field of computer vision and contributed to significant advancements in image understanding and analysis.

The MNIST dataset is a widely used benchmark dataset in the field of computer vision and machine learning. It consists of a large collection of handwritten digit images, each labelled with the corresponding digit from 0 to 9. The MNIST dataset is often used as a starting point for learning and practising various computer vision tasks and algorithms.

The dataset contains a training set with 60,000 images and a test set with 10,000 images. Each image in the dataset is a grayscale image of size 28x28 pixels. The images are preprocessed and normalized to have pixel values between 0 and 1, making them suitable for training machine learning models.

The MNIST dataset serves as an excellent resource for developing and evaluating models for tasks like image classification, where the goal is to classify the input image into one of the ten possible classes (digits 0 to 9). It provides a simple yet challenging task that allows researchers and practitioners to explore various techniques and algorithms in the field of computer vision.

In the context of computer vision, neurons refer to the computational units within a neural network that are responsible for processing and analysing visual information. These neurons are inspired by the structure and functioning of biological neurons in the human visual system.

In a neural network, each neuron receives inputs from the preceding layer and performs a weighted sum of these inputs. The weighted sum is then passed through an activation function, which introduces non-linearity and enables the network to learn complex patterns and representations.

In the case of computer vision, the input to the neurons is typically a visual stimulus, such as an image or a part of an image. The neurons in the initial layers of the network detect low-level visual features, such as edges, corners, and textures. As the signal propagates through the network, the neurons in the deeper layers learn to detect higher-level features and complex visual patterns.

CNNs are particularly effective in computer vision tasks because they are specifically designed to leverage the spatial structure and local connections present in images. The convolutional layers perform feature extraction by convolving learned filters with the input image, while the pooling layers downsample the extracted features. The fully connected layers then combine the features and make predictions.

The neurons in a computer vision neural network play a crucial role in learning and recognizing visual patterns and objects. By adjusting the weights and biases of the neurons during the training process, the network learns to accurately classify and identify objects, and perform object detection, segmentation, and other computer vision tasks.

Designing a neural network for computer vision involves several key considerations to ensure its effectiveness in learning and recognizing visual patterns. Here are some important factors to consider when designing a neural network for computer vision tasks:

• Architecture: Choose an appropriate architecture that suits the specific task at hand. CNNs are commonly used in computer vision due to their ability to effectively capture spatial hierarchies and local dependencies in images. CNNs typically consist of multiple convolutional layers, pooling layers, and fully connected layers.
• Input size: Determine the input size that matches the dimensions of the images in the dataset. This ensures compatibility between the network’s architecture and the input data.
• Convolutional layers: Decide on the number and size of convolutional layers. Deeper networks with more convolutional layers can learn more complex and abstract features, but they also require more computational resources.
• Pooling layers: Determine the type and size of pooling layers to downsample the features and increase the network’s spatial invariance. Common pooling operations include max pooling and average pooling.
• Activation functions: Choose appropriate activation functions for the neurons in the network. Common choices include ReLU (Rectified Linear Unit) for hidden layers and softmax for the output layer in classification tasks.
• Number of neurons: Decide on the number of neurons in each layer. This depends on the complexity of the task and the size of the dataset. Too few neurons may result in underfitting, while too many neurons may lead to overfitting.
• Regularization techniques: Apply regularization techniques such as dropout or L2 regularization to prevent overfitting and improve the generalization ability of the network.
• Optimization algorithm: Select an optimization algorithm, such as stochastic gradient descent (SGD) or Adam, to train the network and update the weights and biases.
• Loss function: Choose an appropriate loss function that aligns with the objective of the task. For classification tasks, cross-entropy loss is commonly used.
• Hyperparameter tuning: Experiment with different hyperparameters, such as learning rate, batch size, and regularization strength, to find the optimal settings for the network.

Designing a neural network for computer vision is an iterative process that involves experimentation, analysis of performance, and fine-tuning based on the specific requirements and characteristics of the task. It requires a combination of domain knowledge, experience, and understanding of the underlying principles of neural networks and computer vision.

Normalization, in the context of neural networks and machine learning, refers to the process of scaling input data to a standard range or distribution. It is an important preprocessing step that helps in improving the performance and convergence of the neural network.

Normalization is important for the following reasons:

• Data range: Normalizing the input data ensures that all features have a similar scale and range. This is crucial because features with larger scales can dominate the learning process and lead to biased weight updates. By normalizing the data, we bring all features to a similar scale, preventing this issue and allowing the network to learn from all features equally.
• Gradient descent optimization: Many optimization algorithms, such as gradient descent, work more efficiently when the features are on a similar scale. Normalization helps in achieving this balance by reducing the variation in feature values. It allows the optimization algorithm to converge faster and find a more optimal solution.
• Avoiding numerical instability: In some cases, when dealing with large values or very small values, numerical instability can occur during the training process. Normalizing the data helps in avoiding these numerical instabilities and improves the numerical stability of the computations.
• Regularization: Normalization can be seen as a form of regularization. By constraining the input data to a specific range or distribution, we prevent extreme values that may cause overfitting. It helps in making the model more robust and generalizable.
• Handling different feature units: Normalization is particularly useful when dealing with features that have different units or measurement scales. For example, if one feature is measured in meters and another in kilograms, their ranges can vary significantly. Normalizing the data allows the neural network to handle these different units effectively.

There are different types of normalization techniques including:

• min-max normalization (scaling the data to a specific range)
• z-score normalization (standardizing the data to have zero mean and unit variance)
• feature-wise normalization (normalizing each feature independently).

The choice of normalization technique depends on the specific requirements of the problem and the characteristics of the data.

Overall, normalization plays a vital role in ensuring that the input data is in an appropriate range and distribution for effective learning and generalization in neural networks.

Hyperparameter tuning is the process of finding the optimal values for the hyperparameters of a machine-learning model. Hyperparameters are parameters that are not learned from the data but are set manually before training the model. They control the behaviour of the model and affect its performance, such as the learning rate, the number of hidden layers in a neural network, the regularization strength, and the batch size.

Hyperparameter tuning is necessary because the performance of a machine-learning model can vary significantly with different hyperparameter settings. The goal of hyperparameter tuning is to find the combination of hyperparameters that results in the best performance or minimizes a chosen evaluation metric (such as accuracy or loss).

The process of hyperparameter tuning typically involves the following steps:

1. Define the hyperparameter search space: Determine the range or set of possible values for each hyperparameter that you want to tune.
2. Select an optimization strategy: Choose an optimization strategy to explore the hyperparameter search space. Common strategies include grid search, random search, and more advanced techniques like Bayesian optimization or genetic algorithms.
3. Evaluate models: Train and evaluate the model using different combinations of hyperparameters from the search space. This typically involves training the model on a subset of the data (training set) and evaluating its performance on another subset (validation set).
4. Select the best hyperparameters: Compare the performance of different models and select the hyperparameters that yield the best results according to the chosen evaluation metric.
5. Test the final model: Once the best hyperparameters are determined, test the final model on a separate test set to get an unbiased estimate of its performance.

Hyperparameter tuning is an iterative process that requires experimentation and evaluation of different hyperparameter settings. It is often computationally expensive and time-consuming, especially when dealing with complex models and large datasets. However, finding the optimal hyperparameters can significantly improve the performance and generalization of the model.

It’s worth noting that hyperparameter tuning should be performed with caution and validated properly to avoid overfitting the hyperparameters to the specific dataset. Techniques like cross-validation can help in obtaining a more robust estimate of the model’s performance with different hyperparameter settings.

In the context of machine learning and neural networks, the loss function and optimizer play crucial roles in training a model.

The loss function, also known as the objective function or cost function, quantifies how well the model is performing on the training data. It measures the disparity between the predicted output of the model and the actual target output. The choice of the loss function depends on the nature of the learning task, such as classification or regression.

For example, in classification tasks, common loss functions include:

• Cross-entropy loss: Used for multi-class classification problems, it measures the dissimilarity between the predicted class probabilities and the true class labels.
• Binary cross-entropy loss: Used for binary classification problems, it measures the dissimilarity between the predicted probabilities of the positive class and the true binary labels.
• Hinge loss: Used in support vector machines (SVMs) and for binary classification, it encourages correct classification with a margin.

In regression tasks, common loss functions include:

• Mean squared error (MSE): Measures the average squared difference between the predicted and true continuous values.
• Mean absolute error (MAE): Measures the average absolute difference between the predicted and true values, providing a more robust measure to outliers.

The optimizer is responsible for updating the model’s parameters during the training process to minimize the loss function. It determines how the model adjusts its internal weights and biases based on the gradients of the loss function. The goal is to find the optimal set of parameters that minimize the loss and improve the model’s performance.

Some commonly used optimizers include:

• Stochastic gradient descent (SGD): An iterative optimization algorithm that updates the parameters in the direction of the steepest gradient of the loss function.
• Adam: An adaptive optimization algorithm that combines the advantages of adaptive learning rates and momentum-based updates.
• RMSprop: Another adaptive optimization algorithm that adjusts the learning rate based on the average of recent squared gradients.

The choice of the loss function and optimizer depends on the specific learning task and the characteristics of the data. Experimentation and fine-tuning of these components are essential to achieve optimal model performance. Different combinations of loss functions and optimizers may yield different training dynamics and convergence behaviours.

The following code example demonstrates the process of training a neural network using the TensorFlow framework.

Coding activity

Try out the coding activity below. The code can be accessed here.

1. Load the MNIST dataset, which consists of images of handwritten digits and their corresponding labels. 
2. Preprocess the data by scaling the pixel values to the range of 0 to 1.
3. Define the architecture of our neural network using the Sequential class from Keras. The model consists of a flatten layer that converts the 2D image data into a 1D vector, followed by a fully connected (dense) layer with 128 units and ReLU activation, and finally, an output layer with 10 units and softmax activation, which outputs the predicted probabilities for each class.
4. Compile the model by specifying the optimizer, loss function, and evaluation metric. In this case, we use the Adam optimizer, sparse categorical cross-entropy loss, and accuracy metric.
5. Train the model using the fit() method, passing in the training data, labels, number of epochs, and batch size. We also provide the validation data to evaluate the model's performance on unseen data during training.

By running this code, the model will undergo the training process, where it learns to make predictions on the MNIST dataset. The training is performed for 10 epochs, with each batch containing 32 samples. The model's accuracy and loss will be displayed for each epoch, and the final performance on the validation set will be reported.

Note: This is a simplified example for demonstration purposes. In practice, you may need to apply additional techniques such as data augmentation, and regularization.

To evaluate the trained model and check its accuracy on the test data, you can use the evaluate method in TensorFlow.

Coding activity

Try out the coding activity below. The code can be accessed here.

1. The evaluate method is used to compute the loss and accuracy of the model on the test data. The method takes the test data (x_test) and their corresponding labels (y_test) as input.
2. The evaluate method returns the test loss and test accuracy. These values can be stored in variables (in this case, test_loss and test_accuracy) for further analysis or printing.
3. Finally, you can print the test accuracy using print('Test Accuracy:', test_accuracy). This will display the accuracy of the model's predictions on the test data.

By running this code after training the model, you can assess the model's performance on unseen data and determine its accuracy in classifying the test examples.

The accuracy of classification in machine learning models, refers to the measure of how well the model is able to correctly classify the input data into their respective categories or classes. Accuracy is often expressed as a percentage and provides an indication of the model's performance.

In the context of evaluating the accuracy of a classification model, it is common to use metrics such as accuracy score, precision, recall, and F1 score. These metrics provide insights into different aspects of the model's performance and can help in assessing its effectiveness.

Accuracy score

This metric calculates the percentage of correctly classified instances out of the total number of instances. It is a simple and intuitive measure of classification accuracy. However, it may not provide a complete picture, especially when dealing with imbalanced datasets where the classes are not evenly represented.

Precision

Precision measures the proportion of correctly predicted positive instances (true positives) out of all instances predicted as positive (true positives + false positives). It indicates the model's ability to avoid false positives.

Recall

Recall, also known as sensitivity or true positive rate, measures the proportion of correctly predicted positive instances (true positives) out of all actual positive instances (true positives + false negatives). It indicates the model's ability to detect all positive instances.

F1 score

The F1 score is the harmonic mean of precision and recall. It provides a balanced measure that considers both precision and recall. It is useful when the dataset is imbalanced or when there is an uneven cost associated with false positives and false negatives.

When discussing the accuracy of the classification, it is important to consider the context of the problem, the specific requirements or constraints, and the characteristics of the dataset. Accuracy alone may not be sufficient to evaluate the performance of a classification model, and it should be interpreted alongside other metrics to gain a comprehensive understanding of the model's effectiveness in classification tasks.

A callback is a technique that allows you to customize the behaviour of the training process and perform certain actions at specific points during training. Callbacks provide a way to monitor and control the model's training process dynamically.

Callbacks are functions or objects that are passed to the training process to be executed at different stages or conditions. They can be used to perform tasks such as saving model checkpoints, adjusting learning rates, logging training metrics, early stopping, and more. Here are a few common use cases for callbacks:

• Model checkpointing: Callbacks can be used to save the model's weights or entire model at certain intervals during training. This allows you to restore the model later or use it for inference.
• Early stopping: Callbacks can monitor the validation loss or other metrics and stop the training process early if the performance on the validation set is not improving. This helps prevent overfitting and saves computational resources.
• Learning rate scheduling: Callbacks can modify the learning rate during training, either based on predefined schedules or dynamically based on the model's performance. This can help in achieving faster convergence or better optimization.
• Logging and visualization: Callbacks can be used to log training metrics, such as loss and accuracy, to track the model's performance over time. They can also visualize these metrics using tools like TensorBoard.

Callbacks provide flexibility and allow you to extend the functionality of the training process without modifying the core training loop. They can be easily integrated into popular deep learning frameworks like TensorFlow and Keras, where they are implemented as classes that inherit from predefined callback base classes.

By using callbacks effectively, you can improve the training process, monitor the model's progress, and make dynamic adjustments to enhance the model's performance.

Implementing a CNN involves constructing a network architecture that consists of convolutional layers, pooling layers, fully connected layers, and activation functions.

To detect features using convolution and create a CNN, follow these steps:

1. Import the necessary libraries: Start by importing the required libraries, such as TensorFlow or Keras, for implementing the CNN.
2. Load and preprocess the data: Prepare your dataset by loading the images and performing any necessary preprocessing steps, such as resizing, normalization, or data augmentation.
3. Define the convolutional layers: Create the convolutional layers in your CNN architecture. Each convolutional layer consists of a set of learnable filters that scan the input image to detect specific features. Specify the number of filters, kernel size, padding, and stride for each layer.
4. Apply activation functions: After each convolutional layer, apply an activation function, such as ReLU (rectified linear unit), to introduce non-linearity into the network and enable better feature representation.
5. Add pooling layers: Insert pooling layers, such as MaxPooling or AveragePooling, to downsample the feature maps and reduce spatial dimensions. Pooling helps to extract the most important features while reducing the computational complexity of the network.
6. Flatten the feature maps: Flatten the output of the last convolutional layer into a 1-dimensional vector. This prepares the data for input to the fully connected layers.
7. Add fully connected layers: Introduce fully connected layers, also known as dense layers, to perform classification or regression based on the learned features. Specify the number of neurons in each layer and apply activation functions as needed.
8. Compile the model: Configure the model by specifying the loss function, optimizer, and evaluation metrics. This step determines how the model will be trained and optimized.
9. Train the model: Use the training dataset to train the CNN. Fit the model to the training data by specifying the number of epochs and batch size. During training, the model will learn to detect features through the convolutional layers and optimize the weights through backpropagation.
10. Evaluate the model: Evaluate the performance of the trained CNN using the testing dataset. Compute metrics such as accuracy, precision, recall, or F1 score to assess how well the model performs on unseen data.
11. Make predictions: Use the trained CNN to make predictions on new, unseen data. Input the test data into the model and obtain the predicted outputs.
12. Fine-tune and iterate: Fine-tune the CNN by adjusting hyperparameters, experimenting with different architectures, or incorporating regularization techniques to improve performance. Iterate through training, evaluation, and adjustment steps to optimize the model.

By following these steps, you can detect features using convolution and create a CNN that learns to recognize and classify patterns in images. The convolutional layers play a crucial role in extracting meaningful features, while the subsequent layers help in classification or regression based on those features.

Watch the following video explaining CNNs.

Pooling, in the context of CNNs, is a technique used to downsample the feature maps generated by convolutional layers. It reduces the spatial dimensions of the input while retaining the most important information.

The main purpose of pooling is two-fold:

• Dimensionality reduction: By downsampling the feature maps, pooling reduces the number of parameters and computations required in the network. This helps in reducing overfitting and makes the model more computationally efficient.
• Translation invariance: Pooling makes the CNN more robust to small spatial variations in the input data. By summarizing local features, pooling helps the network to focus on the most salient features while being less sensitive to their exact locations in the image.

The two most commonly used pooling operations are MaxPooling and AveragePooling:

• MaxPooling: This operation selects the maximum value within each local region of the feature map. It retains the strongest feature response in that region, providing a form of spatial pooling.
• AveragePooling: This operation computes the average value within each local region of the feature map. It summarizes the information in the region, providing a form of spatial averaging.

Pooling is typically applied after one or more convolutional layers. It involves specifying the pool size (e.g., 2x2 or 3x3) and stride (the amount by which the pooling window moves) to determine the amount of downsampling. Common choices for pool size and stride are (2, 2) or (3, 3), which reduce the spatial dimensions by half.

The pooling operation is performed independently for each feature map/channel, allowing the network to capture the most relevant features across different channels. It reduces the spatial dimensions while retaining the most salient information, helping the subsequent layers in the network focus on high-level features.

Overall, pooling is an important technique in CNNs as it helps to reduce computational complexity, control overfitting, and improve the network's ability to extract meaningful features from the input data.

In the context of CNNs, the .Conv2D layer is used for performing convolution operations on the input data. It is one of the key building blocks of CNNs and plays a crucial role in extracting features from images or other grid-like data.

The .Conv2D layer takes as input a multi-dimensional array, typically representing an image or a feature map, and applies a set of filters to perform convolution. Each filter consists of a set of learnable weights (also known as kernels) that are convolved with the input data. This process involves element-wise multiplication of the filter weights with the corresponding input values, followed by summation to produce a single value in the output feature map.

The key parameters of the .Conv2D layers are as follows:

• Filters: It specifies the number of filters to be applied in the layer. Each filter captures a different feature or pattern in the input data.
• Kernel_size: It defines the size of the filters, typically specified as a tuple (height, width). The kernel size determines the receptive field of the filters.
• Strides: It specifies the stride length, which determines the step size at which the filters are applied to the input data during convolution. By default, it is set to (1, 1).
• Padding: It determines the padding strategy to be used during convolution. Valid padding (default) means no padding is added, while the same padding adds zeros around the input to maintain the spatial dimensions.
• Activation: It specifies the activation function to be applied to the output of the convolution operation.

The .Conv2D layer is typically followed by non-linear activation functions such as ReLU (Rectified Linear Unit) to introduce non-linearity and increase the expressive power of the network.

Coding activity: .Conv2D layer in TensorFlow

Here's an example code snippet that demonstrates how to use the .Conv2D layer in TensorFlow.

The code shown below can be accessed here. 

1. In this example, input_data represents the input tensor to the Conv2D layer, and output is the result of applying the convolution operation.
2. The output will have the shape (batch_size, output_height, output_width, filters), where output_height and output_width depend on the input size, kernel size, strides, and padding.
3. By stacking multiple Conv2D layers, along with pooling layers, activation functions, and other components, you can build more complex CNN architectures for tasks such as image classification, object detection, and image segmentation.

Coding activity: CNN in TensorFlow

Here's an example code snippet that demonstrates the implementation of a CNN using TensorFlow with the model summary.

The code shown below can be accessed here. 

1. The CNN model consists of multiple layers, including Conv2D, MaxPooling2D, Flatten, and Dense layers.
2. The input shape of the first Conv2D layer is (32, 32, 3), indicating a 32x32 RGB image as input.
3. The subsequent layers progressively extract features and perform pooling operations to reduce the spatial dimensions.
4. The Flatten layer converts the 2D feature maps into a 1D vector, which is then passed through fully connected Dense layers for classification.
5. The output layer has 10 units with a softmax activation function for multi-class classification.

After defining the model architecture, the model is compiled using the Adam optimizer and the sparse categorical cross-entropy loss function. Finally, the model.summary() function is called to display the summary of the model, which provides information about the number of parameters in each layer and the overall structure of the model.

By running this code, you can see the model summary printed in the console, which gives a concise overview of the layers, their output shapes, and the total number of parameters in the model.

Dense layers, also known as fully connected layers, are a type of layer commonly used in neural network architectures. In a dense layer, each neuron is connected to every neuron in the previous layer, forming a fully connected network. Dense layers play a crucial role in capturing complex patterns and relationships in the data.

In a dense layer, each neuron receives inputs from all the neurons in the previous layer and applies a set of learnable weights to these inputs. These weights determine the strength and importance of each input connection. The output of a neuron in a dense layer is computed by applying an activation function to the weighted sum of its inputs.

The purpose of dense layers is to extract high-level features and perform non-linear transformations on the input data. They are typically placed after convolutional and pooling layers in CNN architectures, as they help to capture spatial patterns and learn representations from the extracted features. In the case of recurrent neural networks (RNNs), dense layers are often used after the recurrent layers to process the sequence information.

The number of neurons in a dense layer and the number of dense layers themselves are hyperparameters that can be adjusted to control the capacity and complexity of the model. More neurons or deeper layers allow the model to learn more intricate representations but also increase the computational requirements and the risk of overfitting.

Overall, dense layers provide flexibility and expressive power to neural networks, allowing them to model complex relationships in the data and make accurate predictions.

• CNNs are widely used in computer vision for tasks such as image classification, object detection, and image segmentation.
• CNNs consist of convolutional layers that extract local features from input images through convolutions with learnable filters.
• Non-linear activation functions like ReLU introduce non-linearities to capture complex relationships in the data.
• Pooling layers help downsample feature maps and reduce spatial dimensions to make the network more robust and computationally efficient.
• Fully connected layers capture global relationships and make predictions based on the extracted features.
• CNNs are trained through backpropagation, adjusting the weights and biases to minimize a loss function.
• Data augmentation techniques can be applied to improve model generalization and prevent overfitting.
• Transfer learning using pre-trained models can provide a starting point for training CNNs and leverage learned features from large datasets.
• The implementation process involves configuring the architecture, training the network on labelled data, and fine-tuning hyperparameters for optimal performance.

Here's an example code snippet for implementing a CNN using the CIFAR-10 dataset in Python with TensorFlow and Keras.

Coding activity

Try out the coding activity below. The code can be accessed here. 

1. Load the CIFAR-10 dataset and normalize the pixel values between 0 and 1.
2. Define the CNN architecture using the Sequential API in Keras. The architecture includes convolutional layers, max-pooling layers, and fully connected layers.
3. Compile the model with an optimizer, a loss function, and evaluation metrics.
4. Train the model on the training dataset and validate it on a subset of the data.
5. Evaluate the trained model on the test dataset to measure its performance.
    

Note that this is a basic example to get you started. You can further enhance the model by adding more layers, adjusting hyperparameters, or applying regularization techniques based on the specific requirements of your computer vision task.