Introduction to Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed to process data with a grid-like structure. They are most commonly applied to image-related tasks such as image classification, object detection, and semantic segmentation.
1. Basic Concept
The core idea of CNN is to use the convolution operation to extract features from input data. This method is particularly effective for image data, as it can capture local patterns such as edges, corners, and other structural details.
2. CNN Architecture
The entire CNN computation process can be divided into two main tasks: Convolution and Pooling.
Convolution
The purpose of convolution is to extract local features from the input data (for example, a bird’s beak as a distinctive feature of a bird). Convolutional layers use filters (kernels) to capture these features such as edges and textures, forming the foundation for later recognition.
Source: Analog Devices
Pooling
The main purpose of pooling is to reduce the size of the image, which speeds up computation and helps the model remain stable when recognizing important features. Regardless of where the features appear or how they slightly change, the model can still detect them accurately.
As shown below, the goal is to extract the feature of a dog's eye. After simplification by the filter, the essential feature remains while the amount of data to process is greatly reduced.
Source: A improved pooling method for convolutional neural networks
3. Typical Model Applications
- ResNet (Deep Residual Network)
- YOLO (You Only Look Once - Real-time Object Detection)
- Faster R-CNN (Region-based Convolutional Neural Network)
Introduction to Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed to process data with a grid-like structure. They are most commonly applied to image-related tasks such as image classification, object detection, and semantic segmentation.
1. Basic Concept
The core idea of CNN is to use the convolution operation to extract features from input data. This method is particularly effective for image data, as it can capture local patterns such as edges, corners, and other structural details.
2. CNN Architecture
The entire CNN computation process can be divided into two main tasks: Convolution and Pooling.
Convolution
The purpose of convolution is to extract local features from the input data (for example, a bird’s beak as a distinctive feature of a bird). Convolutional layers use filters (kernels) to capture these features such as edges and textures, forming the foundation for later recognition.
Source: Analog Devices
Pooling
The main purpose of pooling is to reduce the size of the image, which speeds up computation and helps the model remain stable when recognizing important features. Regardless of where the features appear or how they slightly change, the model can still detect them accurately.
As shown below, the goal is to extract the feature of a dog's eye. After simplification by the filter, the essential feature remains while the amount of data to process is greatly reduced.
Source: A improved pooling method for convolutional neural networks
3. Typical Model Applications
- ResNet (Deep Residual Network)
- YOLO (You Only Look Once - Real-time Object Detection)
- Faster R-CNN (Region-based Convolutional Neural Network)
Introduction to Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed to process data with a grid-like structure. They are most commonly applied to image-related tasks such as image classification, object detection, and semantic segmentation.
1. Basic Concept
The core idea of CNN is to use the convolution operation to extract features from input data. This method is particularly effective for image data, as it can capture local patterns such as edges, corners, and other structural details.
2. CNN Architecture
The entire CNN computation process can be divided into two main tasks: Convolution and Pooling.
Convolution
The purpose of convolution is to extract local features from the input data (for example, a bird’s beak as a distinctive feature of a bird). Convolutional layers use filters (kernels) to capture these features such as edges and textures, forming the foundation for later recognition.
Source: Analog Devices
Pooling
The main purpose of pooling is to reduce the size of the image, which speeds up computation and helps the model remain stable when recognizing important features. Regardless of where the features appear or how they slightly change, the model can still detect them accurately.
As shown below, the goal is to extract the feature of a dog's eye. After simplification by the filter, the essential feature remains while the amount of data to process is greatly reduced.
Source: A improved pooling method for convolutional neural networks
3. Typical Model Applications
- ResNet (Deep Residual Network)
- YOLO (You Only Look Once - Real-time Object Detection)
- Faster R-CNN (Region-based Convolutional Neural Network)
Introduction to Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed to process data with a grid-like structure. They are most commonly applied to image-related tasks such as image classification, object detection, and semantic segmentation.
1. Basic Concept
The core idea of CNN is to use the convolution operation to extract features from input data. This method is particularly effective for image data, as it can capture local patterns such as edges, corners, and other structural details.
2. CNN Architecture
The entire CNN computation process can be divided into two main tasks: Convolution and Pooling.
Convolution
The purpose of convolution is to extract local features from the input data (for example, a bird’s beak as a distinctive feature of a bird). Convolutional layers use filters (kernels) to capture these features such as edges and textures, forming the foundation for later recognition.
Source: Analog Devices
Pooling
The main purpose of pooling is to reduce the size of the image, which speeds up computation and helps the model remain stable when recognizing important features. Regardless of where the features appear or how they slightly change, the model can still detect them accurately.
As shown below, the goal is to extract the feature of a dog's eye. After simplification by the filter, the essential feature remains while the amount of data to process is greatly reduced.
Source: A improved pooling method for convolutional neural networks
3. Typical Model Applications
- ResNet (Deep Residual Network)
- YOLO (You Only Look Once - Real-time Object Detection)
- Faster R-CNN (Region-based Convolutional Neural Network)
