DevFest Tokyo 2017 TensorFlow

DevFest Tokyo 2017で発表した「TensorFlowで
趣味の画像収集サーバーを作る 2017年10月号」の資料です。

SSD（Single Shot MultiBox Detector）によるイラスト顔検出について。

Transcript

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11. γεςϜߏ੒  Downloader Face Detection Megane Detection ֬ೝɾमਖ਼ ೝࣝ݁Ռ JSON ֶशʢ܇࿅ʣ λΠϜϥΠϯ ϝσΟΞ σʔληοτ ֶशʢ܇࿅ʣ TensorFlow 
12. { "generator": "Region Cropper", "file_name": "haruki_g17.png", "regions": [ { "probability": 1.0, "label": 2, "rect": { "left": 97.0, "top": 251.0, "right": 285.0, "bottom": 383.0 } }, { "probability": 1.0, "label": 2, "rect": { "left": 536.0, "top": 175.0, "right": 730.0, "bottom": 321.0 } } ] } Region Cropper: https://github.com/keiji/region_cropper 
13. ؟ڸ່ͬ൑ఆ conv 3x3x3 32 conv 3x3x32 64 max_pooling lrn conv 3x3x64
 128 conv 3x3x128 128 max_pooling lrn conv 3x3x128
 128 conv 3x3x128 256 fc 384 fc 256 output 2 
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    ̍ˍ̎  https://techbookfest.org/tbf01/#A-28 https://techbookfest.org/event/tbf02/circle/5705718560718848
16. ੔ཧ଴ͪͷը૾   ຕ  ʢ2017.10.08 ࣌఺ʣ
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19. γεςϜߏ੒  Downloader Face Detection Megane Detection ֬ೝɾमਖ਼ ೝࣝ݁Ռ JSON ֶशʢ܇࿅ʣ λΠϜϥΠϯ ϝσΟΞ σʔληοτ ֶशʢ܇࿅ʣ TensorFlow 
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24. ֶशσʔλͷ࡞੒
25. ࠲ඪͷਖ਼ଇԽɹਖ਼نԽ 888x613 left : 536 → 0.6036 top : 175 → 0.2854 right : 730 → 0.8220 bottom : 321 → 0.5236 0.1092 ← 97 : left 0.4094 ← 251 : top 0.3209 ← 285 : right 0.6247 ← 383 : bottom 
26. ը૾ͷϦαΠζɾάϨʔεέʔϧԽ 256x256 left : 0.6036 top : 0.2854 right : 0.8220 bottom : 0.5236 0.1092 : left 0.4094 : top 0.3209 : right 0.6247 : bottom 
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29. Ϟσϧ conv 5x5x1
 128 batch_normalization conv 1x1x128
 64 conv 3x3x64
 64 batch_normalization conv 1x1x64
 128 ReLU conv 1x1x128
 128 conv 3x3x128
 128 batch_normalization conv 1x1x128
 128 ReLU conv 1x1x256
 256 conv 3x3x256
 256 batch_normalization conv 1x1x256
 128 ReLU conv 2x2x128
 128 fc 2048 fc 4 256x256x1 Residual block 1 Residual block 2 Residual block 3 × 3 × 3 × 3 avg_pool 2x2 avg_pool 2x2 avg_pool 2x2 ReLU Sigmoid 
30. ೖྗͱग़ྗ 256x256x1 Ϟσϧ Sigmoid 
31. ReLU Sigmoid ׆ੑԽؔ਺ 
32. 256x256 left : 0.6036 top : 0.2854 right : 0.8220 bottom : 0.5236 0.1092 : left 0.4094 : top 0.3209 : right 0.6247 : bottom ࠲ඪͷ஋Ҭʢ
    
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33. ReLU Sigmoid ׆ੑԽؔ਺ 
34. ܇࿅ɾֶश ϛχόον
    
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35. ݕग़݁Ռ 
36. ̎ਓҎ্͍Δ৔߹͸ʁ 
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38. σʔλ಺༁  0 (18.57%) 1 (57.64%) 2 (11.93%) إͷ਺ ϑΝΠϧ਺ ׂ߹ 0 3748 18.57% 1 11636 57.64% 2 2408 11.93% 3 844 4.18% 4 596 2.95% 5 325 1.61% 6 214 1.06% 7 108 0.53%
39. ೖྗͱग़ྗ 256x256x1 Ϟσϧ Sigmoid 
40. إྖҬͷݕग़
41. إྖҬͷݕग़ dconv 3x3x1 64 dconv 3x3x64 64 dconv 3x3x256 128 dconv 3x3x64 128 dconv 3x3x128 128 conv 3x3x1 64 conv 3x3x64 64 conv 3x3x128 256 conv 3x3x64 128 conv 3x3x128 128 
42. إྖҬͷݕग़ 
43. ... ৞ΈࠐΈ૚ʢDPOWEʣͷ໾ׂ  ಛ௃Ϛοϓ
44. إྖҬͷݕग़ʢ৞ΈࠐΈΦʔτΤϯίʔμʣ dconv 3x3x3 64 dconv 3x3x64 64 dconv 3x3x256 128 dconv 3x3x64 128 dconv 3x3x128 128 conv 3x3x1 64 conv 3x3x64 64 conv 3x3x128 256 conv 3x3x64 128 conv 3x3x128 128 
45. ৞ΈࠐΈ૚ͷΈʹͨ͠৔߹ conv 3x3x1 64 conv 3x3x64 64 conv 3x3x128 256 conv 3x3x64 128 conv 3x3x128 128 dconv 3x3x3 64 dconv 3x3x64 64 dconv 3x3x256 128 dconv 3x3x64 128 dconv 3x3x128 128 
46. ৞ΈࠐΈ૚ͷΈʹͨ͠৔߹ conv 3x3x64 stride 1 conv 3x3x64
 stride 1 ReLU ReLU conv 3x3x128
 stride 1 conv 3x3x128
 stride 1 ReLU conv 3x3x256
 stride 1 conv 3x3x256
 stride 1 ReLU conv 1x1x1
 stride 1 256x256x1 max_pool 2x2 stride 2 max_pool 2x2 stride 2 ReLU ReLU Sigmoid 29x29x1 
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49. ࠲ඪͷதԝͷηϧΛબ୒ 
50. ֤ηϧʹσʔλΛઃఆ  confidence : 1.0 center_x : 0.7128 center_y : 0.4045 width : 0.2184 height : 0.2382 1.0 : confidence 0.2150 : center_x 0.5169 : center_y 0.2117 : width 0.2151 : height confidence : 0.0 center_x : 0.0 center_y : 0.0 width : 0.0 height : 0.0
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52. Ϟσϧ conv 3x3x64 stride 1 conv 3x3x64
 stride 1 ReLU ReLU conv 3x3x128
 stride 1 conv 3x3x128
 stride 1 ReLU conv 3x3x256
 stride 1 conv 3x3x256
 stride 1 ReLU conv 1x1x5
 stride 1 256x256x1 max_pool 2x2 stride 2 max_pool 2x2 stride 2 ReLU ReLU Sigmoid 29x29x5 
53. ग़ྗ  confidence center_x center_y width height 29 × 29 × (4 + 1) = 4,205
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 stride 1 ReLU ReLU conv 3x3x128
 stride 1 conv 3x3x128
 stride 1 ReLU conv 3x3x256
 stride 1 conv 3x3x256
 stride 1 ReLU conv 1x1x1
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57. ... લ૚͔ΒͷೖྗΛεΩϟϯͯ͠ಛ௃ϚοϓΛग़ྗ  ಛ௃Ϛοϓ
58. ಛ௃Ϛοϓ͸ɺ
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59. ಛ௃Ϛοϓ͸ɺ
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60. దͨ͠αΠζͷηϧʹׂΓ౰ͯΔ 
61. ৞ΈࠐΈΛ܁Γฦ͢աఔͰग़ྗ͢Δ  Single Shot MultiBox Detector https://arxiv.org/abs/1512.02325
62.  Ϟσϧ
63. conv 3x3x64 stride 1 conv 3x3x64
 stride 1 ReLU ReLU conv 3x3x128
 stride 1 conv 3x3x128
 stride 1 ReLU conv 3x3x256
 stride 1 conv 3x3x256
 stride 1 ReLU 256x256x1 max_pool 2x2 stride 2 max_pool 2x2 stride 2 ReLU ReLU 29x29x256
64. conv 3x3x256
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 stride 2 conv 1x1x5
 stride 1 Sigmoid conv 1x1x5
 stride 1 conv 1x1x5
 stride 1 Sigmoid conv 1x1x5
 stride 1 Sigmoid conv 1x1x5
 stride 1 Sigmoid 1x1x5 15x15x5 8x8x5 2x2x5 4x4x5 29x29x256 conv 1x1x5
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 stride 2 conv 3x3x256
 stride 2 Sigmoid 29x29x5
65.  ֶशσʔλ
66. ֶशσʔλ  confidence : 1.0 center_x : 0.7128 center_y : 0.4045 width : 0.2184 height : 0.2382 1.0 : confidence 0.2150 : center_x 0.5169 : center_y 0.2117 : width 0.2151 : height confidence : 0.0 center_x : 0.0 center_y : 0.0 width : 0.0 height : 0.0
67. ηϧͷׂΓ౰ͯج४ ÷ = Jaccard Overlap ҰఆҎ্ͷ஋ʢॏͳΓʣͷηϧʹׂΓ౰ͯ ↓ + - ) ( 
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70. def _smooth_l1_loss(x): with tf.name_scope("smooth_l1"): abs_x = tf.abs(x) less_mask = tf.cast(abs_x < 1.0, tf.float32) return less_mask * (0.5 * tf.square(x)) + (1.0 - less_mask) * (abs_x - 0.5) def _loc_loss(logits, groundtruth): offset = logits - groundtruth return _smooth_l1_loss(offset) 
71. MSEʢMean Squared Errorʣ Smooth L1 Loss 
72.  ֶशʹࣦഊʜʜ
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    -JDFOTF https://speakerdeck.com/keiji/devfest-tokyo-2017-tensorflow