Training and validation losses for different architectures of AEs.

NIAID Data Ecosystem2026-05-01 收录

数据链接：

https://figshare.com/articles/dataset/Training_and_validation_losses_for_different_architectures_of_AEs_/24388068 数据链接链接失效反馈

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资源简介：

Training and validation losses for different architectures of AEs.

创建时间：

2023-10-19

ResNet34

医学图像处理

深度学习模型比较

方法本研究收集了1420张舌象。经过筛选，专家对它们进行了判断，并对舌头图像进行了注释，以形成舌头图像数据集。然后将基于深度学习卷积神经网络 (CNN) 的人工智能网络框架，即ResNet34应用于该数据集，自动提取图像特征，实现舌图像分类。最后，将VGG16网络框架应用于数据集，对分类模型进行比较，并与分类效果进行比较。结果本文通过对标注采集的舌图像进行整理，形成了相关的数据集，验证了ResNe

OpenDataLab2026-07-12 更新860

Traffic Speed Prediction with Deep Learning Methods - Best results

流量预测

深度学习模型比较

In recent decades, traffic problems have become more severe due to population growth in urban areas and the associated number of cars and motorbikes. This has led to an increase in problems related to

DataCite Commons2022-10-25 更新40

Factors Associated with Physician Agreement and Coding Choices of Cause of Death Using Verbal Autopsies for 1130 Maternal Deaths in India

孕产妇死因编码

编码性能评估

BackgroundThe Indian Sample Registration System (SRS) with verbal autopsy methods provides estimations of cause specific mortality for maternal deaths, where the majority of deaths occur at home, unre

NIAID Data Ecosystem30

rSDE-Bench

需求评估

编码性能评估

该数据集是一个面向需求导向的软件开发基准，包含了复杂多样的软件需求，并具备对需求正确性进行自动评估的功能。此外，该自动评估指标（准确性）与人工评估高度一致，相关系数达到了0.9922。该数据集的任务是对软件需求进行评估，同时考察编码能力。

arXiv90

Evaluation scores of attention mechanism comparison experiment.

注意力机制

深度学习模型比较

Evaluation scores of attention mechanism comparison experiment.

NIAID Data Ecosystem30