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efficientdet

EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow

efficientdet-1

(Pretrained weights provided) EfficientDet: Scalable and Efficient Object Detection implementation by Signatrix GmbH

efficientdet.pytorch

Implementation EfficientDet: Scalable and Efficient Object Detection in PyTorch

ensemblesvm

A Library for Ensemble Learning Using Support Vector Machines

enviplugins

The latest versions of my various ENVI plugins

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

examples-1

Learn to create a desktop app with Python and Qt

exercise

exercise for nndl

fastbook

Draft of the fastai book

fb.resnet.torch

Torch implementation of ResNet from http://arxiv.org/abs/1512.03385 and training scripts

fewshotpapers

This repository contains few-shot learning (FSL) papers mentioned in our FSL survey.

fine-tuning-a-pre-trained-cnn-for-first-year-sea-ice-and-multi-year-sea-ice-cp-imagery-classificatio

Mapping first-year sea ice and multi-year sea ice in the oceans is significant for many applications. For example, ship navigation and weather forecast. Accurate and robust classification methods of multi-year ice and first-year ice are in demand [2]. Hybrid-polarity SAR architecture will be included in future SAR missions such as the Canadian RADARSAT Constellation Mission (RCM). These sensors will enable the use of compact polarimetry (CP) data in wide swath imagery [1]. Convolutional neural networks (CNNs) are becoming increasingly popular in many research communities due to availability of large image datasets and high-performance computing systems. As Convolutional networks (ConvNets) have achieved great success on many image classification tasks, I pursue this method for the classification of image patches from compact polarimety (CP) imagery into first-year ice and multi-year ice is applicable. In this course project, my work is kind of like the first practice of the CP imagery classification by fine-tuning a pre-trained convolutional neural network (CNN). Specifically, fine-tuning the last fully-connected layer of a pre-trained convolutional networks, I extract patches from simulated CP images as my dataset, the classification accuracy of the test set achieved 91.3% by fine-tuning a pre-trained CNN, compared to 49.4% classification accuracy by training from scratch.

fitlog

fitlog是一款在深度学习训练中用于辅助用户记录日志和管理代码的工具

fucking-algorithm

手把手撕LeetCode题目，扒各种算法套路的裤子。English version supported! Crack LeetCode, not only how, but also why.

geospatial-machine-learning

A curated list of resources focused on Machine Learning in Geospatial Data Science.

gmt

The Generic Mapping Tools

goagent-always-available

一直可用的GoAgent，会定时扫描可用的google gae ip，goagent会保持更新

growth

Growth - Be Awesome Developer & Awesome Hacker

gsr

Matlab code for Group-based Sparse Representation for Image Restoration

gurls

GURLS: a Least Squares Library for Supervised Learning

halfrost-field

✍️ 这里是写博客的地方 —— Halfrost-Field 冰霜之地

howtobeaprogrammer

A guide on how to be a Programmer - originally published by Robert L Read

hsid-cnn

Q. Yuan, Q. Zhang, J. Li, H. Shen, and L. Zhang, "Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network," IEEE TGRS, 2019.

hyperspectral-image-target-detection-based-on-sparse-representation

Hyperspectral image Target Detection based on Sparse Representation

icode-mda

Automatically exported from code.google.com/p/icode-mda

id-cnn

SAR Image Despeckling Using a Convolutional Neural Network

image-denoising-state-of-the-art

image-matching-benchmark

Public release of the Image Matching Benchmark: https://vision.uvic.ca/image-matching-challenge

info8010-deep-learning

Lectures for INFO8010 - Deep Learning, ULiège, Gilles Louppe个人官网http://glouppe.github.io/：www.montefiore.ulg.ac.be/~glouppe

insarzd

An InSAR Processor for ALOS-2 Multi-Mode SAR Data and Ionospheric Correction