Paper address: https://arxiv.org/pdf/2212.12741.pdf Code address: https://github.com/SanaNazari/LMFLoss 1.What is it? LMFLOSS is a hybrid loss function for imbalanced medical image classification. It is composed of a linear combination of Focal Loss and LDAM Loss and is designed to better handle imbalanced data sets. Focal Loss improves model performance by emphasizing hard-to-classify samples, while LDAM Loss takes […]
Tag: loss
Hair loss secrets: the most complete summary of front-end Chrome debugging skills
Click on the front-end Q above and follow the public account Reply to join the group and join the front-end Q technology exchange group Author: An Muxi Original text: https://juejin.cn/post/7248118049584316472 Note: The tests and screenshots in this article are all from the Edge browser (the kernel is Chromium). For the browser kernel, you can learn […]
C#Json string to DataTable, DataSet JsonConvert.DeserializeObject loss of precision problem
C# Json string to DataTable, DataSet JsonConvert.DeserializeObject loss of precision problem Problem description When I was converting Json string to DataTable, I found that if I have a column of decimals, but the first row is an integer, all decimal digits below will be lost. Example public const string _InvoiceDataJsonTable = “[{“EXCHANGE_USD”:24},{“EXCHANGE_USD”:15},{“EXCHANGE_USD”: 191.06}]”; DataTable dtTable […]
Prevent message loss and message duplication – Kafka reliability analysis and optimization practice
Directory of series articles The first step to get started is to teach you step by step how to install kafka and the visualization tool kafka-eagle. What is Kafka and how to use SpringBoot to connect to Kafka Necessary capabilities for architecture-kafka selection comparison and application scenarios Kafka access principle and implementation analysis to break […]
Hair loss secrets: the most complete summary of front-end Chrome debugging skills
Author: An Muxi Original text: https://juejin.cn/post/7248118049584316472 Note: The tests and screenshots in this article are all from the Edge browser (the kernel is Chromium). For the browser kernel, you can learn about “What browsers/kernels are there?” [1]》 00. Summary of basic operations Operation type Shortcut keys/instructions Switch browser tabs Ctrl + 1 to 8Switch to […]
Java: Use the BigDecimal class to cleverly handle the precision loss of the Double type
Key points of this article Briefly describe the reasons why precision is lost when converting floating point from decimal to binary. Introduce the differences between several ways to create BigDecimal. A collection of tools for high-precision calculations. I learned the provisions of Alibaba Java Development Manual on BigDecimal equality. Classic problem: loss of precision of […]
Similarity loss summary, pytorch code
Used to constrain image generation as loss. Gradient optimization pytorch structural similarity (SSIM) loss https://github.com/Po-Hsun-Su/pytorch-ssim https://github.com/harveyslash/Facial-Similarity-with-Siamese-Networks-in-Pytorch/blob/master/Siamese-networks-medium.ipynb class ContrastiveLoss(torch.nn.Module): “”” Contrastive loss function. Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf “”” def __init__(self, margin=2.0): super(ContrastiveLoss, self).__init__() self.margin = margin def forward(self, output1, output2, label): euclidean_distance = F.pairwise_distance(output1, output2, keepdim = True) loss_contrastive = torch.mean((1-label) * torch.pow(euclidean_distance, 2) + (label) * […]
A must-have for YOLOv5 to gain points! Improved loss functions EIoU, SIoU, AlphaIoU, FocalEIoU, Wise-IoU
Table of Contents 1. The role of improving the loss function Second, specific implementation 1. Improving the role of loss function The role of the YOLOv5 loss function is to measure the difference between the predicted box and the real box, and update the parameters of the model based on these differences. It helps the […]
Classification Network-FocalLoss of Class Imbalance Problem
The training and testing codes are as follows: (The complete code comes from CNN from construction to deployment actual combat) train.py import torch import torchvision import time import argparse import importlib from loss import FocalLoss def parse_args(): parser = argparse.ArgumentParser(‘training’) parser.add_argument(‘–batch_size’, default=128, type=int, help=’batch size in training’) parser.add_argument(‘–num_epochs’, default=5, type=int, help=’number of epoch in training’) […]
Label smooth loss label smooth loss function (with code)
Paper address: https://proceedings.neurips.cc/paper_files/paper/2019/file/f1748d6b0fd9d439f71450117eba2725-Paper.pdf 1.What is it? Label smooth loss is a regularization method to prevent overfitting. It is improved on the basis of traditional classification loss using softmax loss. Traditional softmax loss uses hard label for one hot encoding, while label smooth loss adds a little noise to the one hot encoding obtained by hard […]