How to Build a Convolutional Neural Network for Computer Vision?

In today’s world, computer vision has become an integral part of many industries, including healthcare, retail and automotive. Convolutional neural networks (CNNs) are widely used for computer vision tasks such as image classification, object detection, and segmentation. In this article, we walk you through the process of creating a computer vision CNN with code examples.

Step 1: Understanding CNNs

Before diving into building a CNN, it’s essential to understand the basics of CNNs. CNNs are deep learning models inspired by the structure and function of the human visual system. They consist of convolutional layers, pooling layers, and fully connected layers. Convolutional layers extract features from the input image, pooling layers reduce the spatial dimensions of the features, and fully connected layers classify the image.

Step 2: Collecting Data

The first step of creating a CNN is data collection. The more data you have, the better your CNN model will perform. In computer vision tasks, information is usually in the form of images. You can collect data from open-source datasets or create your own dataset. If you decide to create a dataset, make sure it is diverse and balanced.

Step 3: Preprocessing Data

After data collection, the next step is data processing. Preprocessing involves resizing the images to a uniform size, normalizing pixel values, and dividing the data into training, validation, and test sets. It is important to properly pre-process the data, as this can significantly affect the performance of the CNN.

Step 4: Building the CNN

Now it’s time to build the CNN. We will be using the Keras library, which is a high-level neural network API written in Python. Here’s an example of a CNN architecture for image classification.

This architecture consists of three convolutional layers, each followed by a max pooling layer, and two fully connected layers. The last layer uses the softmax activation function to output the predicted class probabilities.

Step 5: Training the CNN

After building the CNN, the next step is to train the CNN. We use the Adam optimizer, which is an adaptive learning rate optimization algorithm, and a categorical cross-entropy loss function, which is used for multi-class classification problems. Here is an example of fitting and training a CNN.

Step 6: Evaluating the CNN

After training the CNN, the next step is to evaluate the CNN. We use a test set to evaluate the performance of CNN. let’s see an example of CNN’s evaluation.

Step 7: Fine-tuning the CNN

After evaluating the CNN, the next step is fine-tuning the CNN. Fine-tuning involves adjusting the hyperparameters and weights of the pre-trained CNN. Fine-tuning is done by freezing the early layers of the network and only updating the later layers. This is because the earlier layers of the network are responsible for detecting low-level features, which are common across all image recognition tasks. On the other hand, the later layers of the network are responsible for detecting high-level features, which are more specific to the task at hand. To fine-tune a CNN, one can use a pre-trained network, adjust the optimizer, and train on a smaller dataset to improve the performance. By fine-tuning the CNN, one can improve the accuracy of the network and make it more suitable for the specific task at hand.


In this article we discussed about building CNN step-by-step and implemented in Python language. If you have any question, let me know about it.

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