In previous articles, we have introduced two types of image segmentation loss functions. Today, we will share commonly used multi-class image segmentation loss functions such as multi-class cross-entropy, weighted multi-class cross-entropy, multi-class Dice coefficient, multi-class Focal Loss, etc., and provide code to reproduce the above loss functions in TensorFlow. 1. Cross Entropy The cross-entropy loss … Read more
Commonly used image segmentation loss functions include binary cross-entropy, dice coefficient, Tversky, and Focal Loss. Today, I will reproduce the above loss functions in TensorFlow and compare the results. 1. Cross Entropy The cross-entropy loss function compares the predicted class values with the target values on a pixel-by-pixel basis, and then averages the values over … Read more
Click the "Little White Learns Vision" above, choose to add "Star" or "Top" Heavy content delivered to you immediately For academic sharing only, does not represent the position of this public account, please contact for deletion if there is any infringement Reprinted from:Author:mingo_ζ Original link: https://blog.csdn.net/shanglianlm/article/details/85019768 Introduction This article summarizes nineteen loss functions, introducing their … Read more
Source: Algorithm Advancement This article is about 1800 words, suggested reading time is 8 minutes. This article introduces you to different types of loss functions. Source: CSDN-mingo_ζ Address: https://blog.csdn.net/shanglianlm/article/details/85019768 Basic Usage criterion = LossCriterion() # The constructor has its own parameters loss = criterion(x, y) # Call standard also has parameters 19 Types of Loss … Read more
Source: Pythonic Biologist This article is about 1900 words long, and it is recommended to read it in 8 minutes. TensorFlow and PyTorch are quite similar; this article introduces loss functions using PyTorch as an example. 19 Types of Loss Functions 1. L1 Loss L1Loss Calculates the absolute difference between output and target. torch.nn.L1Loss(reduction='mean') Parameters: … Read more
Researchers from the South China University of Technology have published a paper that describes the mathematical principles of convolutional networks. This paper explains the operations and propagation processes of convolutional networks from a mathematical perspective.This paper is very helpful for understanding the mathematical essence of convolutional networks and assists readers in implementing convolutional networks “by … Read more
Recently, researchers from Nanyang Technological University published a paper that describes the mathematical principles of convolutional networks. This paper explains the operations and propagation processes of convolutional networks from a mathematical perspective. It is very helpful for understanding the mathematical essence of convolutional networks and aids readers in implementing convolutional networks “from scratch” (without using … Read more