As data scientists, we are constantly exploring new techniques and algorithms to improve the accuracy and efficiency of our models. When it comes to image-related problems, convolutional neural networks (CNNs) are an essential tool in our arsenal. CNNs have proven to be highly effective for tasks such as image classification and segmentation, and have even been used in cutting-edge applications such as self-driving cars and medical imaging. Convolutional neural networks (CNNs) are deep neural networks that have the capability to classify and segment images. CNNs can be trained using supervised or unsupervised machine learning methods, depending on what you want them to do. CNN architectures for classification and segmentation include a variety of different layers with specific purposes, such as a convolutional layer, pooling layer, fully connected layers, dropout layers, etc.
In this blog post, we will dive into the basic architecture of CNNs for classification and segmentation. We will cover the fundamentals of how CNNs work, including convolutional layers, pooling layers, and fully connected layers. By the end of this blog post, you will have a solid understanding of how CNNs can be used to tackle image-related problems and will be ready to apply this knowledge in your own projects. So, whether you’re a seasoned data scientist or just starting out, join us on this journey to discover the power of CNNs in image analysis.
Convolutional Neural Networks (CNNs) are a type of deep learning algorithm that have been developed specifically to work with images and other grid-like data, such as audio signals and time series data. The CNN architecture for image classification includes convolutional layers, max-pooling layers, and fully connected layers. The following is a description of different layers of CNN:
The diagram below represents the basic CNN architecture for image classification.
In the above architecture, the following are different layers:
The examples of classification learning task where CNN is used are image classification, object detection, and facial recognition.
Computer vision deals with images, and image segmentation is one of the most important steps. It involves dividing a visual input into segments to make image analysis easier. Segments are made up of sets of one or more pixels. Image segmentation sorts pixels into larger components while also eliminating the need to consider each pixel as a unit. It is the process of dividing image into manageable sections or “tiles”. The process of image segmentation starts with defining small regions on an image that should not be divided. These regions are called seeds, and the position of these seeds defines the tiles. The picture below can be used to understand image classification, object detection and image segmentation. Notice how image segmentation can be used for image classification or object detection.
Image segmentation has two levels of granularity. They are as following:
Semantic segmentation classifies image pixels into one or more classes which are semantically interpretable, rather, real-world objects. Categorizing the pixel values into distinct groups using CNN is known as region proposal and annotation. Region proposals are also called candidate objects patches, which can be thought of as small groups of pixels that are likely to belong to the same object.
CNNs for semantic segmentation typically use a fully convolutional network (FCN) architecture, which replaces the fully connected layers of a traditional CNN with convolutional layers. This allows the network to process input images of any size and produce a corresponding output map of the same size, where each pixel is assigned a label.
The FCN architecture typically consists of an encoder-decoder structure with skip connections. The encoder consists of a series of convolutional and pooling layers, which gradually reduce the spatial resolution of the feature maps while increasing the number of channels. The decoder consists of a series of up sampling layers, which gradually increase the spatial resolution of the feature maps while reducing the number of channels. The skip connections allow the network to incorporate high-level features from the encoder into the decoding process, improving the accuracy of the segmentation.
During training, the network is typically trained using cross-entropy loss, which measures the difference between the predicted segmentation and the ground truth segmentation. The ground truth segmentation is a binary mask where each pixel is labeled with its corresponding class label.
During inference, the FCN takes an input image and produces a corresponding output map where each pixel is assigned a label. The output map can then be post-processed to produce a binary mask for each class, which identifies the pixels belonging to that class.
In case of instance segmentation, each instance of each object is identified. Instance segmentation requires the use of an object detection algorithm in addition to the CNN architecture. There are different approaches to doing instance based segmentation. They are as following:
This type of segmentation is important in many computer vision applications, including autonomous driving, robotics, and medical image analysis.
Semantic segmentation is the task of assigning a class label to each pixel in an image, where the label corresponds to a semantic category such as “person,” “car,” or “tree.” In other words, semantic segmentation aims to divide an image into regions that correspond to different object categories or classes, but does not differentiate between different instances of the same object class. For example, all instances of “person” in an image would be labeled with the same “person” class label.
Instance-based segmentation, on the other hand, is the task of identifying and separating each instance of an object in an image. This means that each object instance is assigned a unique label, and each pixel is labeled according to the object instance it belongs to. For example, in an image with multiple people, each person would be labeled with a different instance ID, and pixels belonging to each person would be labeled with the corresponding ID.
Here is a view of how encoder-decoder FCN architecture is used for image segmentation:
CNNs are a powerful tool for segmentation and classification, but they’re not the only way to do these tasks. CNN architectures have two primary types: segmentations CNNs that identify regions in an image from one or more classes of semantically interpretable objects, and classification CNNs that classify each pixel into one or more classes given a set of real-world object categories. In this article we covered some important points about CNNs including how they work, what their typical architecture looks like, and which applications use them most frequently. Do you want help with understanding more about CNN architectures? Let us know!
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