Deep Learning for Image Recognition in Python

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"Deep Learning for Image Recognition in Python" at PyCon JP 2014 https://pycon.jp/2014/schedule/presentation/20/ Youtube http://youtu.be/JWGXQhVHTTA Keywords Machine Learning, Object Recognition, Face Recognition, Artificial Intelligence (AI) ディープラーニング, 深層学習, 機械学習, 画像認識, 物体認識, 顔認識, 人工知能

Technology

Deep Learning for Image Recognition in Python
x
Hideki Tanaka PyCon JP 2014

Agenda
1. Image Recognition䛳䛶ఱ?
2. Deep Learning䛳䛶ఱ?
3. Pepperྩ䛻䛧䛶ḧ䛧䛔䛣䛸

୍⯡ⓗ䛺≉ᚩ㔞
SURF
Haar-like features
Python䝷䜲䝤䝷䝸
OpenCV
Mahotas

Training set
Test set
: 25,000 images
: 12,500 images

Core
Language Binding
Theano/Pylearn2
cuda-convnet
OverFeat
Caffe
DeCAF
Python
C++
Lua
C++
Python
-
Python
Python
Python
-
Pre-trained
Networks
Framework
×
×
䕿
䕿
䕿

Pre-trained Networks
ImageNet Large Scale Visual Recognition Challenge
䛾䛯䜑䛻Deep Learning䛾◊✲⪅䛜సᡂ䛧䛯䝛䝑䝖䝽䞊䜽
䠄Deep Convolutional Neural Networks䠅

Pre-trained Networks䜢⏝䛔䛶≉ᚩ㔞䜢ᢳฟ
(DeCAF)
ᶵᲔᏛ⩦
(scikit-learn)

Deep Learning䛷ฟ᮶䜛䛣䛸
㧗⢭ᗘ䛾⏬ീㄆ㆑
Pepperྩ䛿ぢ䛯䜒䛾䜢䜋䜌㛫㐪䛔䛺䛟ㄆ㆑

1. 䜰䜲䝗䝹䛾㢦⏬ീ䜢㞟䜑䜛
2. 䜰䜲䝗䝹௨እ䛾㢦⏬ീ䜢㞟䜑䜛
3. Deep Learning䛷ㄆ㆑ჾ䜢స䜛
4. Pepperྩ䛻ᶵ⬟䜢ᦚ㍕䛩䜛
5. Pepperྩ䛜䝯䞁䝞䞊䜢䝇䜹䜴䝖䛩䜛

㛤Ⓨ⎔ቃ
Ubuntu 12.04
pyenv + miniconda–3.4.2 (Python 2.7.8)
Flask, OpenCV, DeCAF

➲ᑿ࿴ᏹ䚷ᒣᮏග✑䚷ዉⰋ㒊㝯⾜䚷㧗ᶫඞᚨ䚷᳃⏣┤ᶞ
⏣୰⚽ᶞ䚷ఀ⸨♸┿䚷ᅧྜྷ㞞ே䚷ⱑᮌ㱟䚷ὸ㔝᠇┬
఍⏣ᐥḟ㑻䚷఍⏣⿱Ꮚ䚷ᰩཎ୍㈗

The esperanto generator
torajiro aida
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Wee are discussing 20 best applications of deep learning with Python, that you must know. Let’s discuss them one by one: i. Restoring Color in B&W Photos and Videos With Deep Learning, it is possible to restore color in black and white photos and videos. This can give a new life to such media. The ACM Digital Library is one such project that colorizes grayscale images combining global priors and local image features. This is based on Convolutional Neural Networks. The Deep Learning network learns patterns that naturally occur within photos. This includes blue skies, white and gray clouds, and the greens of grasses. It uses past experience to learn this. Although sometimes, it can make mistakes, it is efficient and accurate most of the times.

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電子情報通信学会「パターン認識・メディア理解研究会」（2016年2月14日＠九州工業大学，福岡県飯塚市)でのプレゼン資料です．対応する原稿は以下です．電子情報通信学会技術研究報告, PRMU2015-133 http://www.ieice.org/ken/paper/20160221UbGo/ 以下はアブストラクトです．=========================== 印刷数字，手書き数字，多フォント数字を対象として，畳み込みニューラルネットワーク(CNN) による認識実験を試みた．いずれのタスクにも大規模なデータセットを用いた．得られた認識率は，印刷数字について99.99%，手書き数字について99.89%，そして多フォント数字について96.4%であった．さらに印刷数字と手書き数字の混合認識という，予想される困難性からか従来あまり試みられなかった課題についても，CNNの利用により99.92%の認識率を得た．以上の実験を通して，人間の認識能力に近い性能が得られるようになった事実を確認した上で，本稿ではさらに今後の研究課題としてどのような方向性がありうるか，私見を述べたい．

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Deep Learning for Image Recognition in Python

1. Deep Learning for Image Recognition in Python x Hideki Tanaka PyCon JP 2014

2. PyCon䛳䛶ఱ?

4. PyCon䛿ྜ䝁䞁䛷䛿䛒䜚䜎䛫䜣 #pyconjp

5. @atelierhide

6. @atelierhide = ?

7. @atelierhide = Lens Designer

9. @atelierhide = Photographer

10.

11. @atelierhide = Pythonista

12.

13. @atelierhide = Kaggler

14.

15. ᮏ᪥䛾䛚㢟䛿…

16. Image Recognition x Deep Learning

17. ⏬ീㄆ㆑ x ῝ᒙᏛ⩦

18. ேཱྀ▱⬟(AI)䛾䛚ヰ䛷䛩

19. ேཱྀ▱⬟(AI)䛸ゝ䛘䜀…

20. Pepperྩ

21. 䛣䛾䝉䝑䝅䝵䞁䛷ⓙ䛥䜣䛸⪃䛘䛯䛔 1 䛯䛳䛯䜂䛸䛴䛾䛣䛸

22. Pepperྩ䛻ఱ䜢䛧䛶ḧ䛧䛔䛛?

23. Agenda 1. Image Recognition䛳䛶ఱ? 2. Deep Learning䛳䛶ఱ? 3. Pepperྩ䛻䛧䛶ḧ䛧䛔䛣䛸

24. Image Recognition䛳䛶ఱ?

25. Dog or Cat?

26.

27.

28. 䛹䛖䜔䛳䛶ㄆ㆑䛩䜛?

29. ⏬ീ䛛䜙≉ᚩ㔞䜢ᢳฟ ᶵᲔᏛ⩦ (scikit-learn)

30. ୍⯡ⓗ䛺≉ᚩ㔞 SURF Haar-like features Python䝷䜲䝤䝷䝸 OpenCV Mahotas

31. Training set Test set : 25,000 images : 12,500 images

32.

33. Accuracy 60% with Haar-like features

34. 䛧䛛䛧䚸䝇䝁䜰䝪䞊䝗䛷䛿…

35. Accuracy >95% with Deep Learning

36. 98.5%!?

37. Deep Learning䛩䛢䛗䟿

38. Deep Learning䛳䛶ఱ?

39.

40. ከᒙ䝙䝳䞊䝷䝹䝛䝑䝖䝽䞊䜽 input output hidden × n

41. Core Language Binding Theano/Pylearn2 cuda-convnet OverFeat Caffe DeCAF Python C++ Lua C++ Python - Python Python Python - Pre-trained Networks Framework × × 䕿䕿䕿

42. Pre-trained Networks ImageNet Large Scale Visual Recognition Challenge 䛾䛯䜑䛻Deep Learning䛾◊✲⪅䛜సᡂ䛧䛯䝛䝑䝖䝽䞊䜽䠄Deep Convolutional Neural Networks䠅

43. Pre-trained Networks䜢⏝䛔䛶≉ᚩ㔞䜢ᢳฟ (DeCAF) ᶵᲔᏛ⩦ (scikit-learn)

44. Accuracy 96% with Deep Learning

45.

46. 䛣䛣䜎䛷䛾ヰ䜢䜎䛸䜑䜛䛸…

47. Deep Learning䛷ฟ᮶䜛䛣䛸㧗⢭ᗘ䛾⏬ീㄆ㆑ Pepperྩ䛿ぢ䛯䜒䛾䜢䜋䜌㛫㐪䛔䛺䛟ㄆ㆑

48. 䛷䚸Pepperྩ䛻ఱ䜢䛧䛶ḧ䛧䛔䛛?

49. Pepperྩ䛜䜽䝸䜶䜲䝔䜱䝤䛺௙஦䜢䛩䜛 ♫఍ⓗ䛺䜲䞁䝟䜽䝖

50. Pepperྩ䝥䝻䝕䝳䞊䝇䛾䜰䜲䝗䝹䜾䝹䞊䝥

51. ୡ⏺ึ! ேཱྀ▱⬟䛻䜘䜛䜰䜲䝗䝹䜾䝹䞊䝥!

52. 䛭䛾ྡ䜒…

53. JKC48