Name: Li Pan
Type: User
Company: Southeastern University
Bio: Li Pan is a PhD student at Southeast University, with research interests including small target detection, multi-target tracking, time series prediction, etc.
Location: Nanjing,China
Li Pan's Projects
飞桨常规赛:PALM眼底彩照视盘探测与分割基线方案-6月第六名方案-采用U-net结构
:notebook_with_decorative_cover: 简书文章中的材料
【干货】史上最全的PyTorch学习资源汇总
🕶 A curated list of Tiny Object Detection papers and related resources.
Homepage for STAT 157 at UC Berkeley
Algorithm & DataSet for Machine Learning
U-Net implementation in PyTorch for FLAIR abnormality segmentation in brain MRI
船长关于机器学习、计算机视觉和工程技术的总结和分享
Completed the CS231n 2017 spring assignments from Stanford university
《动手学深度学习》:面向中文读者、能运行、可讨论。英文版即伯克利“深度学习导论”教材。
deep learning for image processing including classification and object-detection etc.
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
深度学习入门开源书,基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.
A paper list of object detection using deep learning.
深度有趣
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系[email protected] 版权所有,违权必究 Tan 2018.06
Deformable Convolutional Networks
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet代码实现改为PyTorch实现。
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现,项目已得到李沐老师的同意
Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋
Deep Q-learning for playing flappy bird game
Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.
Collection of publicly available IPTV channels from all over the world
李宏毅《机器学习》笔记,在线阅读地址:https://datawhalechina.github.io/leeml-notes
Pytorch implementation of the MARL algorithm, MADDPG, which correspondings to the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments".
Code for the Make Your Own Neural Network book
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.