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

2. PyCon?

3. PyCon #pyconjp

4. @atelierhide

5. @atelierhide = ?

6. @atelierhide = Lens Designer

7. @atelierhide = Photographer

8. @atelierhide = Pythonista

9. @atelierhide = Kaggler

10.

11. Image Recognition x Deep Learning

12. x

13. (AI)

14. (AI)

15. Pepper

16. 1

17. Pepper?

18. Agenda 1. Image Recognition? 2. Deep Learning? 3. Pepper

19. Image Recognition?

20. Dog or Cat?

21. ?

22. (scikit-learn)

23. SURF Haar-like features Python OpenCV Mahotas

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

25. Accuracy 60% with Haar-like features

26.

27. Accuracy >95% with Deep Learning

28. 98.5%!?

29. Deep Learning

30. Deep Learning?

31. input output hidden n

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

33. Pre-trained Networks ImageNet Large Scale Visual Recognition Challenge Deep Learning Deep Convolutional Neural Networks

34. Pre-trained Networks (DeCAF) (scikit-learn)

35. Accuracy 96% with Deep Learning

36.

37. Deep Learning Pepper

38. Pepper?

39. Pepper

40. Pepper

41. ! !

42.

43. JKC48

44. JKC48 48

45. JKC48

46. 1. 2. 3. Deep Learning 4. Pepper 5. Pepper

47.

48. 1. 2. 3. Deep Learning 4. Pepper 5. Pepper

49. Demo

50. JKC48

51. Ubuntu 12.04 pyenv + miniconda3.4.2 (Python 2.7.8) Flask, OpenCV, DeCAF

52. #pyconjp

53.

54. One More Thing

55. !

56.

57.

58.

59. The esperanto generator torajiro aida 11:30- Media Hall

60. !