In self-supervised learning frameworks, deep networks are optimized to align different views of an instance that contains the similar visual semantic information.
For 3-class classification of China, the Top-1 accuracy rate can reach 82. 45\% (train 60\%, test=40\%); For 2-class classification of China, the Top-1 accuracy rate can reach 97. 35\% (train 80\%, test 20\%); For 6-class classification task of world, when the ratio of training set and test set is 20\% : 80\% , the Top-1 accuracy rate can achieve 30. 30\%.
1 code implementation • 16 Sep 2020 • Xuehui Yu, Zhenjun Han, Yuqi Gong, Nan Jiang, Jian Zhao, Qixiang Ye, Jie Chen, Yuan Feng, Bin Zhang, Xiaodi Wang, Ying Xin, Jingwei Liu, Mingyuan Mao, Sheng Xu, Baochang Zhang, Shumin Han, Cheng Gao, Wei Tang, Lizuo Jin, Mingbo Hong, Yuchao Yang, Shuiwang Li, Huan Luo, Qijun Zhao, Humphrey Shi
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
We present an object detection framework based on PaddlePaddle.
Marker family genome sequences play important roles in describing specific microbial clades within species, a framework of support vector machine (SVM) based microbial species classification with N-best algorithm is constructed to classify the centroid marker genome fragments randomly generated from marker genome sequences on MetaRef.
A calculation formula of volume of revolution with integration by parts of definite integral is derived based on monotone function, and extended to a general case that curved trapezoids is determined by continuous, piecewise strictly monotone and differential function.