Ubuntu 미세먼지 팁들
sudo apt-get install lsb-release scrot wget http://github.com/downloads/djmelik/archey/archey-0.2.8.deb sudo dpkg -i archey-0.2.8.deb archey install
이 블로그의 인기 게시물
파이썬으로 Homomorphic Filtering 하기
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...
파이썬으로 Histogram matching 하기.
import numpy as np def hist_match ( source , template ): """ Adjust the pixel values of a grayscale image such that its histogram matches that of a target image Arguments: ----------- source: np.ndarray Image to transform; the histogram is computed over the flattened array template: np.ndarray Template image; can have different dimensions to source Returns: ----------- matched: np.ndarray The transformed output image """ oldshape = source . shape source = source . ravel () template = template . ravel () # get the set of unique pixel values and their corresponding indices and # counts s_values , bin_idx , s_counts = np . unique ( source , return_inverse = True , return_counts = True ) t_values , t_counts = np . unique ( template , return_counts = True ) ...
th main.lua -retrain resnet-50.t7 -data ./data -resetClassifier true -nClasses 80
답글삭제Transfer 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
resnet TL by TF
답글삭제https://github.com/kwotsin/transfer_learning_tutorial.git
https://github.com/guoyongcs/auxnet.git
답글삭제resnet + 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.