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aussc

Alternately Updated Convolutional Spectral-Spatial Network for Hyperspectral Image Classification(AUSSC))

chinese-poetry

The most comprehensive database of Chinese poetry 🧶最全中华古诗词数据库, 唐宋两朝近一万四千古诗人, 接近5.5万首唐诗加26万宋诗. 两宋时期1564位词人，21050首词。

coding-for-bat

comparison-database-of-hyperspectral-classification-results

将各个高光谱图像分类文献（2016-2019，TGRS、JSTARS、GRSL、RS、ISPRS、IJRS、JARS）结果按照同条件进行比较，并进行了总体的评分

compression

Data compression in TensorFlow

cpp_primer_answers

《C++ Primer》第五版中文版习题答案

cv

deepcc

Multi-Target, Multi-Camera Tracking

deeplearning-500-questions

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

dlwpt-code

Code for the book Deep Learning with PyTorch by Eli Stevens and Luca Antiga.

fdssc

A Fast Dense Spectral-Spatial Convolution Network Framework for Hyperspectral Images Classification(Accepted by Remote Sensing)

grebsmo-greedy-bilateral-sketch-completion-smoothing

GreBsmo，Greedy Bilateral Sketch, Completion & Smoothing

hsi_toolkit

GatorSense Hyperspectral Image Analysis Toolkit

image

save the image for Markdown

image-compression

There are some papers about image compression

image-compression-cnn

Semantic JPEG image compression using deep convolutional neural network (CNN)

image_enhancement

imagesegmentation

resources for image segmentation

ir_small_target

resources for IR small target detection

learning_summary

机器学习，深度学习与程序等书籍视频学习笔记与总结汇总

leeml-notes

李宏毅《机器学习》笔记，在线阅读地址：https://datawhalechina.github.io/leeml-notes

lrslibrary

Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos

machinelearning

一些关于机器学习的学习资料与研究介绍

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

mctc4bmi

Matrix and Tensor Completion for Background Model Initialization

modelcompression

multi-object-tracking-paper-list

Paper list and source code for multi-object-tracking

object_detection

pms50

python可视化50图解读（pms50），在线阅读地址：https://datawhalechina.github.io/pms50/