구차니의 잡동사니 모음 :: '프로그램 사용/yolo_tensorflow' 카테고리의 글 목록 (11 Page)

'프로그램 사용/yolo_tensorflow'에 해당되는 글 104건

2021.01.07 CNN - YOLO
2020.10.21 yolo BFLOPs
2020.10.20 yolo3 on ubuntu 18.04
2020.10.06 yolo on rpi?

프로그램 사용/yolo_tensorflow2021. 1. 7. 17:19

CNN - YOLO

[링크 : https://medium.com/curg/you-only-look-once-다-단지-한-번만-보았을-뿐이라구-bddc8e6238e2]

[링크 : https://curt-park.github.io/2017-03-26/yolo/]

[링크 : https://pjreddie.com/darknet/yolo/]

[링크 : https://zeuseyera.github.io/darknet-kr/3_ImageNet_BunRyu/BunRyu.html]

*net?

AlexNet

[링크 : https://en.wikipedia.org/wiki/AlexNet]

VGGNet 이라는 CNN모델

[링크 : https://89douner.tistory.com/61]

LeNet, ZFNet, ...

[링크 : https://blog.naver.com/laonple/221218707503]

inception/googlenet

[링크 : https://ikkison.tistory.com/86]

[링크 : https://kangbk0120.github.io/articles/2018-01/inception-googlenet-review]

net으로 끝나서 먼가 했는데 일단은 이미지 데이터베이스, 단어 데이터베이스 인 듯.

단지 데이터베이스이기 때문에 딥 러닝에 용이해서 언급이 되는 건가?

ImageNet is an image database organized according to the WordNet hierarchy

[링크 : http://www.image-net.org/] imagenet

WordNet® is a large lexical database of English.

[링크 : https://wordnet.princeton.edu/] wordnet

COCO - Common Object in COntext

[링크 : https://cocodataset.org/#home]

저작자표시

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

yolo lite (0)	2021.01.08
SSDnnn (Single Shot Detector) (0)	2021.01.08
yolo BFLOPs (0)	2020.10.21
yolo3 on ubuntu 18.04 (0)	2020.10.20
yolo on rpi? (0)	2020.10.06

Posted by 구차니

프로그램 사용/yolo_tensorflow2020. 10. 21. 11:51

yolo BFLOPs

BFLOPs (Billion FLOPs)

[링크 : https://arxiv.org/pdf/1910.03159.pdf]

저작자표시

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

yolo lite (0)	2021.01.08
SSDnnn (Single Shot Detector) (0)	2021.01.08
CNN - YOLO (0)	2021.01.07
yolo3 on ubuntu 18.04 (0)	2020.10.20
yolo on rpi? (0)	2020.10.06

Posted by 구차니

프로그램 사용/yolo_tensorflow2020. 10. 20. 19:26

yolo3 on ubuntu 18.04

weight 받는데 한참 걸린다(100kbps 정도 뜨는 느낌...)

weight를 받지 않고 실행하면 한참 먼가 계산하고 나서 weight가 없다고 에러나면서 종료된다.

git clone https://github.com/pjreddie/darknet
cd darknet
make
wget https://pjreddie.com/media/files/yolov3.weights
./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

결과물은 prediction.jpg로 박스쳐져서 나온다.(옵션을 주면 상자 위치로 나오려나?)

아무튼 weight 를 받아서 돌려보는데 예상외로 무겁다?

개인 노트북이 i5-2520m 이긴한데 최대 클럭 + 부스트 하도록 설정하고 했는데도 30초 가량 걸린다.

$ time ./darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg
layer     filters    size              input                output
    0 conv     32  3 x 3 / 1   608 x 608 x   3   ->   608 x 608 x  32  0.639 BFLOPs
    1 conv     64  3 x 3 / 2   608 x 608 x  32   ->   304 x 304 x  64  3.407 BFLOPs
    2 conv     32  1 x 1 / 1   304 x 304 x  64   ->   304 x 304 x  32  0.379 BFLOPs
    3 conv     64  3 x 3 / 1   304 x 304 x  32   ->   304 x 304 x  64  3.407 BFLOPs
    4 res    1                 304 x 304 x  64   ->   304 x 304 x  64
    5 conv    128  3 x 3 / 2   304 x 304 x  64   ->   152 x 152 x 128  3.407 BFLOPs
    6 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64  0.379 BFLOPs
    7 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128  3.407 BFLOPs
    8 res    5                 152 x 152 x 128   ->   152 x 152 x 128
    9 conv     64  1 x 1 / 1   152 x 152 x 128   ->   152 x 152 x  64  0.379 BFLOPs
   10 conv    128  3 x 3 / 1   152 x 152 x  64   ->   152 x 152 x 128  3.407 BFLOPs
   11 res    8                 152 x 152 x 128   ->   152 x 152 x 128
   12 conv    256  3 x 3 / 2   152 x 152 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   13 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   14 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   15 res   12                  76 x  76 x 256   ->    76 x  76 x 256
   16 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   17 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   18 res   15                  76 x  76 x 256   ->    76 x  76 x 256
   19 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   20 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   21 res   18                  76 x  76 x 256   ->    76 x  76 x 256
   22 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   23 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   24 res   21                  76 x  76 x 256   ->    76 x  76 x 256
   25 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   26 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   27 res   24                  76 x  76 x 256   ->    76 x  76 x 256
   28 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   29 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   30 res   27                  76 x  76 x 256   ->    76 x  76 x 256
   31 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   32 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   33 res   30                  76 x  76 x 256   ->    76 x  76 x 256
   34 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
   35 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
   36 res   33                  76 x  76 x 256   ->    76 x  76 x 256
   37 conv    512  3 x 3 / 2    76 x  76 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   38 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   39 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   40 res   37                  38 x  38 x 512   ->    38 x  38 x 512
   41 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   42 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   43 res   40                  38 x  38 x 512   ->    38 x  38 x 512
   44 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   45 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   46 res   43                  38 x  38 x 512   ->    38 x  38 x 512
   47 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   48 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   49 res   46                  38 x  38 x 512   ->    38 x  38 x 512
   50 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   51 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   52 res   49                  38 x  38 x 512   ->    38 x  38 x 512
   53 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   54 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   55 res   52                  38 x  38 x 512   ->    38 x  38 x 512
   56 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   57 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   58 res   55                  38 x  38 x 512   ->    38 x  38 x 512
   59 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   60 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   61 res   58                  38 x  38 x 512   ->    38 x  38 x 512
   62 conv   1024  3 x 3 / 2    38 x  38 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   63 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   64 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   65 res   62                  19 x  19 x1024   ->    19 x  19 x1024
   66 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   67 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   68 res   65                  19 x  19 x1024   ->    19 x  19 x1024
   69 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   70 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   71 res   68                  19 x  19 x1024   ->    19 x  19 x1024
   72 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   73 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   74 res   71                  19 x  19 x1024   ->    19 x  19 x1024
   75 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   76 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   77 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   78 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   79 conv    512  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 512  0.379 BFLOPs
   80 conv   1024  3 x 3 / 1    19 x  19 x 512   ->    19 x  19 x1024  3.407 BFLOPs
   81 conv    255  1 x 1 / 1    19 x  19 x1024   ->    19 x  19 x 255  0.189 BFLOPs
   82 yolo
   83 route  79
   84 conv    256  1 x 1 / 1    19 x  19 x 512   ->    19 x  19 x 256  0.095 BFLOPs
   85 upsample            2x    19 x  19 x 256   ->    38 x  38 x 256
   86 route  85 61
   87 conv    256  1 x 1 / 1    38 x  38 x 768   ->    38 x  38 x 256  0.568 BFLOPs
   88 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   89 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   90 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   91 conv    256  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 256  0.379 BFLOPs
   92 conv    512  3 x 3 / 1    38 x  38 x 256   ->    38 x  38 x 512  3.407 BFLOPs
   93 conv    255  1 x 1 / 1    38 x  38 x 512   ->    38 x  38 x 255  0.377 BFLOPs
   94 yolo
   95 route  91
   96 conv    128  1 x 1 / 1    38 x  38 x 256   ->    38 x  38 x 128  0.095 BFLOPs
   97 upsample            2x    38 x  38 x 128   ->    76 x  76 x 128
   98 route  97 36
   99 conv    128  1 x 1 / 1    76 x  76 x 384   ->    76 x  76 x 128  0.568 BFLOPs
  100 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
  101 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
  102 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
  103 conv    128  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 128  0.379 BFLOPs
  104 conv    256  3 x 3 / 1    76 x  76 x 128   ->    76 x  76 x 256  3.407 BFLOPs
  105 conv    255  1 x 1 / 1    76 x  76 x 256   ->    76 x  76 x 255  0.754 BFLOPs
  106 yolo
Loading weights from yolov3.weights...Done!
data/dog.jpg: Predicted in 29.416936 seconds.
dog: 100%
truck: 92%
bicycle: 99%

real	0m33.904s
user	0m32.813s
sys	0m0.600s

tiny가 표준에 비해서 1/30 정도로 가볍긴 하지만, 그렇다고 해서 i5-2520m에서 이정도인데

임베디드 보드에서 실시간은 많이 무리일지도?

$ time ./darknet detect cfg/yolov3-tiny.cfg yolov3.weights data/dog.jpg
layer     filters    size              input                output
    0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16  0.150 BFLOPs
    1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16
    2 conv     32  3 x 3 / 1   208 x 208 x  16   ->   208 x 208 x  32  0.399 BFLOPs
    3 max          2 x 2 / 2   208 x 208 x  32   ->   104 x 104 x  32
    4 conv     64  3 x 3 / 1   104 x 104 x  32   ->   104 x 104 x  64  0.399 BFLOPs
    5 max          2 x 2 / 2   104 x 104 x  64   ->    52 x  52 x  64
    6 conv    128  3 x 3 / 1    52 x  52 x  64   ->    52 x  52 x 128  0.399 BFLOPs
    7 max          2 x 2 / 2    52 x  52 x 128   ->    26 x  26 x 128
    8 conv    256  3 x 3 / 1    26 x  26 x 128   ->    26 x  26 x 256  0.399 BFLOPs
    9 max          2 x 2 / 2    26 x  26 x 256   ->    13 x  13 x 256
   10 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512  0.399 BFLOPs
   11 max          2 x 2 / 1    13 x  13 x 512   ->    13 x  13 x 512
   12 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024  1.595 BFLOPs
   13 conv    256  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 256  0.089 BFLOPs
   14 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512  0.399 BFLOPs
   15 conv    255  1 x 1 / 1    13 x  13 x 512   ->    13 x  13 x 255  0.044 BFLOPs
   16 yolo
   17 route  13
   18 conv    128  1 x 1 / 1    13 x  13 x 256   ->    13 x  13 x 128  0.011 BFLOPs
   19 upsample            2x    13 x  13 x 128   ->    26 x  26 x 128
   20 route  19 8
   21 conv    256  3 x 3 / 1    26 x  26 x 384   ->    26 x  26 x 256  1.196 BFLOPs
   22 conv    255  1 x 1 / 1    26 x  26 x 256   ->    26 x  26 x 255  0.088 BFLOPs
   23 yolo
Loading weights from yolov3.weights...Done!
data/dog.jpg: Predicted in 1.197222 seconds.

real	0m1.811s
user	0m1.750s
sys	0m0.060s

결과가 안나와서 다른 사람들에게 물어보니 tiny용 weight가 따로 있다고 -_ㅠ

wget https://pjreddie.com/media/files/yolov3-tiny.weights
./darknet detect cfg/yolov3-tiny.cfg yolov3-tiny.weights data/dog.jpg

인식은 되는데 시간이 달라지진 않네..

$ time ./darknet detect cfg/yolov3-tiny.cfg yolov3-tiny.weights data/dog.jpg
layer     filters    size              input                output
    0 conv     16  3 x 3 / 1   416 x 416 x   3   ->   416 x 416 x  16  0.150 BFLOPs
    1 max          2 x 2 / 2   416 x 416 x  16   ->   208 x 208 x  16
    2 conv     32  3 x 3 / 1   208 x 208 x  16   ->   208 x 208 x  32  0.399 BFLOPs
    3 max          2 x 2 / 2   208 x 208 x  32   ->   104 x 104 x  32
    4 conv     64  3 x 3 / 1   104 x 104 x  32   ->   104 x 104 x  64  0.399 BFLOPs
    5 max          2 x 2 / 2   104 x 104 x  64   ->    52 x  52 x  64
    6 conv    128  3 x 3 / 1    52 x  52 x  64   ->    52 x  52 x 128  0.399 BFLOPs
    7 max          2 x 2 / 2    52 x  52 x 128   ->    26 x  26 x 128
    8 conv    256  3 x 3 / 1    26 x  26 x 128   ->    26 x  26 x 256  0.399 BFLOPs
    9 max          2 x 2 / 2    26 x  26 x 256   ->    13 x  13 x 256
   10 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512  0.399 BFLOPs
   11 max          2 x 2 / 1    13 x  13 x 512   ->    13 x  13 x 512
   12 conv   1024  3 x 3 / 1    13 x  13 x 512   ->    13 x  13 x1024  1.595 BFLOPs
   13 conv    256  1 x 1 / 1    13 x  13 x1024   ->    13 x  13 x 256  0.089 BFLOPs
   14 conv    512  3 x 3 / 1    13 x  13 x 256   ->    13 x  13 x 512  0.399 BFLOPs
   15 conv    255  1 x 1 / 1    13 x  13 x 512   ->    13 x  13 x 255  0.044 BFLOPs
   16 yolo
   17 route  13
   18 conv    128  1 x 1 / 1    13 x  13 x 256   ->    13 x  13 x 128  0.011 BFLOPs
   19 upsample            2x    13 x  13 x 128   ->    26 x  26 x 128
   20 route  19 8
   21 conv    256  3 x 3 / 1    26 x  26 x 384   ->    26 x  26 x 256  1.196 BFLOPs
   22 conv    255  1 x 1 / 1    26 x  26 x 256   ->    26 x  26 x 255  0.088 BFLOPs
   23 yolo
Loading weights from yolov3-tiny.weights...Done!
data/dog.jpg: Predicted in 1.208611 seconds.
dog: 57%
car: 52%
truck: 56%
car: 62%
bicycle: 59%

real	0m1.822s
user	0m1.770s
sys	0m0.052s

[링크 : https://pjreddie.com/darknet/yolo/]

[링크 : https://blog.insightdatascience.com/how-to-train-your-own-yolov3-detector-from-scratch-224d10e55de2?gi=87339b7b98d4]

+

2020.10.21

[링크 : https://github.com/guichristmann/edge-tpu-tiny-yolo] TPU로 가속은 가능한듯?

+

결과 파일 추가! tiny는 쓸 수 있을까?

yolov3 / kite.jpg

yolov3-tiny / kite.jpg

yolov3 / dog.jpg

yolov3-tiny / dog.jpg

저작자표시

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

yolo lite (0)	2021.01.08
SSDnnn (Single Shot Detector) (0)	2021.01.08
CNN - YOLO (0)	2021.01.07
yolo BFLOPs (0)	2020.10.21
yolo on rpi? (0)	2020.10.06

Posted by 구차니

프로그램 사용/yolo_tensorflow2020. 10. 6. 14:30

yolo on rpi?

[링크 : https://j-remind.tistory.com/53]

[링크 : https://www.pyimagesearch.com/2020/01/27/yolo-and-tiny-yolo-object-detection-on-the-raspberry-pi-and-movidius-ncs/] tiny yolo

저작자표시

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

yolo lite (0)	2021.01.08
SSDnnn (Single Shot Detector) (0)	2021.01.08
CNN - YOLO (0)	2021.01.07
yolo BFLOPs (0)	2020.10.21
yolo3 on ubuntu 18.04 (0)	2020.10.20

Posted by 구차니

«이전 1 ··· 8 9 10 11 다음»

블로그 이미지

구차니

나란히 동등하게 함께

카테고리

잡동사니

Programming

Linux

Linux API

Microsoft

Apple

embeded

하드웨어

이론 관련

회사일

프로그램 사용

3dmax

ajaxplorer

antlr

apache

autocad

audacity

aws

bind

Blender

BOINC - seti@home

busybox

camstudio

CellWriter

ckeditor(웹에디터)

clang

Code::Blocks

coLinux

CRM & ERP

ctags & cscope

CUPS

cunit

Version Control

cygwin & Xming

distcc & ccache

docker

dosbox

doxygen

eclipse

eclipse CDT & minGW

expat & XML

extplorer

fail2ban

ffmpeg & ffserver

fft, fftw

freecad

freedos

freeNAS

fritzing

gcc

gcov, gprof

gdb & insight

GIMP

gitlab

gnuplot

graphviz

gstreamer

hugin

ibatis & mybatis

iconv() & glibc

infrarecoder

iperf

ircd

kinect

kodi(xbmc)

kubernetes

kvm(virt-manager)

LDAP

libfreetype

libjpeg

libmodbus

lighttpd

LVM

make, configure

MAME

Mantis

meld

menuetos

meson

minicom

mysql & mariaDB

nc

ncurses

nfs

nginx

nmap

notepad++

octave

openFOAM

openHPC

openoffice

openrefine

oracle

pfsense

phpmyadmin

poEdit

postgreSQL

proxmox

Putty

PXE(네트워크 부트)

qemu

rtl-sdr

saned

screen

sdl

sed awk regexp

sketchup

SMB(Samba)

sqlite

squid

ssh scp sftp

stellarium

struts2 tiles

synergy

tesseract ocr

Trac

u-boot

uinput

valgrind

vi

VirtualBox & VMware

virtualdub

Visual Studio

Virtual Machine

VLC

VNC

VPN(PPTP)

wayland

webalizer

Webcalendar

wiki

WinE

WinMerge & kdiff3

winscp

wireshark(ethereal)

xen

xrdp

yocto project

yolo_tensorflow

네이버 맵 API

아이나비

유튜브(youtube)

서버 모니터링

한글(HWP)

실패기

google coral

intel ncs2

xournal

모종의 음모

게임

개소리 왈왈

아이디어!

파일방

분류가 모호한 글

공지사항

태그목록

최근에 올라온 글

최근에 달린 댓글

티스토리툴바