How CNNs work – Labeling Image Data Using Data Augmentation

Let us understand how CNNs work for the desired output:

1. Convolution: This is the first step. It’s like moving the small window (filter) over the picture. The filter checks the colors and shapes in the area it’s looking at and learns what’s important.
2. Pooling: After the different parts of the image have been looked at, the CNN doesn’t need all the details. Pooling is like taking a summary of what it’s seen. It simplifies things, but we don’t lose the important parts.
3. Fully connected layers: After looking at the many small parts and summarizing them, everything is connected together next. It’s like putting the pieces of a puzzle together to see the whole picture. This helps the CNN understand the entire image and make a final decision on what is depicted in it.

After the convolutional layers have processed the image by extracting various features and patterns, the fully connected layers play a crucial role in bringing all the information together to make a comprehensive decision about the content of the image. This process is akin to assembling the pieces of a puzzle, where each piece corresponds to a specific feature detected by the convolutional layers. By connecting these pieces, the network gains a holistic understanding of the image.

However, as powerful as fully connected layers are, there is a risk of overfitting, a situation where the model becomes too specialized in the training data and performs poorly on new, unseen data. To mitigate this, regularization techniques are often employed.

4. Regularization in fully connected layers: Regularization is a set of techniques used to prevent overfitting and enhance the generalization capabilities of a model. In the context of fully connected layers, regularization methods are applied to control the complexity of the model and avoid relying too heavily on specific features present in the training data.
5. Training a CNN: To teach a CNN to recognize cats, you’d show it lots of cat pictures. It looks at them, learns the important features, and gets better over time at recognizing them. It also needs to see pictures of things that are not cats, so it can tell the difference.
6. Making predictions: Once a CNN is trained, you can show it a new picture, and it will try to find the important features just like it learned. If it finds enough cat-like features, it will say, “Hey, that’s a cat!”

So, in simple terms, a CNN is like a computer program that learns to recognize things in pictures by looking at small parts of the image, finding important features, and making decisions based on those features.

As we’ve seen, a CNN’s architecture constitutes convolution, pooling, and fully connected layers. The architecture specifies how the model is structured, including the number of layers, the size of filters, and the connections between neurons. The architecture guides how the learned weights and features are used to process images and make predictions.

So, the final model is, in essence, a combination of the architecture, the learned weights, and the learned features. Let’s break down a couple of these elements:

• Learned weights: These are the parameters that the CNN has learned during the training process. The model adjusts these weights to make accurate predictions. These weights are essentially the “knowledge” the model gains during training. They represent how important certain features are for making decisions.
• Learned features: Features, in the context of a CNN, are visual patterns and characteristics of images. They are representations of important information within the image. These features are not directly visible to us but are learned by the network through the layers of convolution and pooling. Features are abstract representations of the image that help the model recognize patterns and objects.

In practice, these learned weights and features are stored in the model’s parameters. When you save a trained CNN model, you are saving these parameters, which can be used to make predictions on new, unseen images. The model takes an image as input, processes it through its layers, and uses the learned weights and features to make predictions, such as classifying objects in the image or detecting specific patterns.

We will now delve into the powerful combination of CNNs and image data augmentation. By artificially augmenting the data, CNNs can be exposed to a broader range of variations during training to help them generalize better to unseen images.

Some of the benefits and considerations of using image data augmentation are reducing overfitting, enhancing model robustness, and improving generalization performance. Whether you are a beginner or an experienced practitioner, this section serves as a comprehensive guide to understanding and implementing image data augmentation in the context of CNNs, assisting you in taking your computer vision projects to new heights.