ResNet 미세먼지 팁들

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https://github.com/aabobakr/fb.resnet.torch.git

 Resnet Transfer MultiGPU Curve version




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  1. 제갈식2017년 8월 8일 PM 10:56

    th main.lua -retrain resnet-50.t7 -data ./data -resetClassifier true -nClasses 80


    Transfer Learning:
    https://github.com/facebook/fb.resnet.torch/tree/master/pretrained

    th classify.lua model_5.t7 ../data/val/Attacks/ics1_ax.png Classes for ../data/val/Attacks/ics1_ax.png


    pretrained/classify.lua 의 n 변경

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  2. 제갈식2017년 8월 8일 PM 11:27

    resnet TL by TF

    https://github.com/kwotsin/transfer_learning_tutorial.git

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  3. 제갈식2017년 8월 8일 PM 11:29

    https://github.com/guoyongcs/auxnet.git

    resnet + aux output = auxNet by torch

    Besides the vanishing gradient problem, very deep networks also suffer from a vanishing supervision signal problem.

    In the course of the investigation we presented a novel model, called AuxNet, which addresses the vanishing supervision signal problem through the use of additional auxiliary outputs.

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파이썬으로 Homomorphic Filtering 하기

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import cv2 # For OpenCV modules (For Image I/O and Contour Finding) import numpy as np # For general purpose array manipulation import scipy . fftpack # For FFT2 #### imclearborder definition def imclearborder ( imgBW , radius ): # Given a black and white image, first find all of its contours imgBWcopy = imgBW . copy () contours , hierarchy = cv2 . findContours ( imgBWcopy . copy (), cv2 . RETR_LIST , cv2 . CHAIN_APPROX_SIMPLE ) # Get dimensions of image imgRows = imgBW . shape [ 0 ] imgCols = imgBW . shape [ 1 ] contourList = [] # ID list of contours that touch the border # For each contour... for idx in np . arange ( len ( contours )): # Get the i'th contour cnt = contours [ idx ] # Look at each point in the contour for pt in cnt : rowCnt = pt [ 0 ][ 1 ] colCnt = pt [ 0 ][ 0 ] # If this is within the radius of th...
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파이썬으로 2D FFT/iFFT 하기: numpy 버전

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import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2 . imread ( 'messi5.jpg' , 0 ) f = np . fft . fft2 ( img ) fshift = np . fft . fftshift ( f ) magnitude_spectrum = 20 * np . log ( np . abs ( fshift )) plt . subplot ( 121 ), plt . imshow ( img , cmap = 'gray' ) plt . title ( 'Input Image' ), plt . xticks ([]), plt . yticks ([]) plt . subplot ( 122 ), plt . imshow ( magnitude_spectrum , cmap = 'gray' ) plt . title ( 'Magnitude Spectrum' ), plt . xticks ([]), plt . yticks ([]) plt . show () rows , cols = img . shape crow , ccol = rows / 2 , cols / 2 fshift [ crow - 30 : crow + 30 , ccol - 30 : ccol + 30 ] = 0 f_ishift = np . fft . ifftshift ( fshift ) img_back = np . fft . ifft2 ( f_ishift ) img_back = np . abs ( img_back ) plt . subplot ( 131 ), plt . imshow ( img , cmap = 'gray' ) plt . title ( 'Input Image' ), plt . xticks ([]), ...
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