Recap of the Previous Lesson and Today’s Theme
In the last lesson, we learned about transfer learning. We understood that transfer learning allows us to apply existing pre-trained models to new tasks, achieving high accuracy with a small amount of data and in a short time. In this lesson, we will learn about Convolutional Neural Networks (CNNs), which are particularly effective in processing image data.
CNNs are very powerful models for image recognition and image classification tasks. Understanding them will reveal how models specialized for images extract features from image data and perform recognition.
What are CNNs (Convolutional Neural Networks)?
Convolutional Neural Networks (CNNs) are deep learning models that are particularly effective for image data. Image data typically consists of thousands to millions of pixels, and it’s important to capture the patterns and relationships in which these pixels are arranged. CNNs are designed to automatically learn these patterns within images and perform classification or object detection.
The most significant feature of CNNs is their ability to efficiently extract image features using layers called convolutional layers when handling image data. Through this feature extraction, they can understand fine details within the image, such as edges, colors, and textures, and ultimately make judgments like “This is a cat” or “This is a car.”
Basic Structure of CNNs
CNNs are mainly composed of the following layers:
1. Convolutional Layer
The core of CNNs is the convolutional layer. Convolutional layers extract image features such as edges and patterns by applying filters (kernels) to the image data. The filters scan parts of the image, emphasizing features and passing them on to the next layer.
To visualize this, consider the convolutional layer as a “magnifying glass.” It observes a part of the image in detail and finds edges and patterns from it.
2. Pooling Layer
The pooling layer performs downsampling, reducing the image size while retaining important features. For example, if an image is 100×100 pixels, passing it through a pooling layer can reduce it to 50×50 pixels. This reduces the computational load without losing essential information.
By using pooling layers, information about “where edges appear” in the image is retained, while “details of the edges themselves” are omitted. In other words, it plays the role of making the model’s computations more efficient while maintaining the overall important features.
3. Fully Connected Layer
Finally, the fully connected layer uses the features extracted by the convolutional and pooling layers to perform the final classification. In this layer, the final prediction is made, such as whether the image is a “cat or a dog” or a “car or a motorcycle.”
In the fully connected layer, each neuron is connected to all the outputs of the previous layer, making decisions based on the image features.
How CNNs Work
Let’s look a little more specifically at how CNNs process images.
- Image Input: For example, let’s say a 28×28 pixel image of a handwritten digit is input.
- Feature Extraction in Convolutional Layers: Convolutional layers apply filters to find features in the image, such as edges, lines, and curves. Each filter emphasizes different features in different parts of the image.
- Reduction in Pooling Layers: The pooling layer receives the output from the convolutional layer and compresses the image features while retaining important information. This improves computational efficiency.
- Classification in Fully Connected Layers: Finally, the fully connected layer uses the output from the pooling layer to predict the category of the image. For example, if the image is a “5”, the final output will predict it as “5”.
Features and Strengths of CNNs
1. Learning While Preserving Spatial Structure
In traditional neural networks, the input data is converted into a single row for processing, losing the spatial relationships between pixels in the image. However, CNNs, through convolution, can process images while preserving the relationships between adjacent pixels. This allows for effective learning of features such as edges and patterns.
2. Improved Computational Efficiency
CNNs process images part by part, rather than the entire image at once, making them computationally more efficient. Additionally, the computational load can be further reduced by using pooling layers to downsample the image data.
3. Highly Adaptable Models
CNNs are applied to many tasks in image recognition. They demonstrate excellent performance in various fields, including handwritten character recognition, object detection, facial recognition, and medical image diagnosis, and their high adaptability is a strength.
Real-World Applications
1. Handwritten Digit Recognition
A typical application of CNNs is the classification task using the MNIST dataset (handwritten digit image data). Using CNNs, handwritten digits from 0 to 9 can be accurately identified, capturing the shapes and edges of the numbers in the image for classification.
2. Object Recognition
CNNs are also widely used in the field of object recognition. For example, the task of detecting and classifying various objects within an image, such as cars, cats, and dogs. In these tasks, CNNs capture the edges and shapes of the image and accurately classify the type of object based on them.
3. Medical Image Diagnosis
In the medical field, diagnostic systems using CNNs are being developed to detect signs of diseases from X-ray and CT scan images. CNNs are useful for detecting subtle abnormalities in medical images, contributing to improving the accuracy of diagnoses.
Summary and Next Lesson
In this lesson, we explained the basics of Convolutional Neural Networks (CNNs), powerful models specialized for image data. CNNs are models that automatically extract patterns and features within images and perform classification and recognition efficiently. You should now understand how layers like convolutional layers and pooling layers process image data and make final predictions.
In the next lesson, we will explain in detail the convolutional layer, the core layer of CNNs. Let’s learn the specific mechanism of how convolutional layers extract image features using filters.
Notes
- Convolutional Layer: A layer in CNNs that extracts features from image data. It uses filters to find edges and patterns.
- Pooling Layer: A layer that compresses the features extracted by the convolutional layer and improves computational efficiency.
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