20180210_Cookpad_TechConf2018_YoheiKIKUTA

Transcript

1. ٠ా ངฏ ݚڀ։ൃ෦ Solve “unsolved” image recognition problems in service

   applications Cookpad Inc. Feb 10th, 2018

2. ࣗݾ঺հ → https://github.com/yoheikikuta/resume ɾ໊લɿ٠ా ངฏ @yohei_kikuta ɾॴଐɿݚڀ։ൃ෦ ɾݞॻɿϦαʔνΤϯδχΞ ɹɹɹɹത࢜ʢཧֶʣ ɾઐ໳ɿը૾෼ੳ

   ɾ޷෺ɿম͖ᰤࢠɺण࢘ɺDr Pepper 2

3. ໨࣍ 3 ɾݚڀ։ൃ෦ͷ঺հ ɾ࣮ۀ຿ʹ͓͚Δը૾෼ੳͷࠔ೉ ɾΫοΫύουͰ۩ମతʹը૾෼ੳʹऔΓ૊ΜͰ͍Δࣄྫͷ঺հ - ྉཧ͖Ζ͘ɿ೚ҙͷը૾ͷྉཧ/ඇྉཧ෼ྨ - Ϩγϐ෼ྨɿྉཧը૾ͷϨγϐΧςΰϦ෼ྨ -

   ϞόΠϧ࣮૷ɿϞόΠϧͰಈ͘ྉཧը૾෼ྨͷϞσϧߏங ɾ·ͱΊ

4. ݚڀ։ൃ෦ͷϝϯόʔ 4 ৽نٕज़Λ׆༻ͨ͠αʔϏεͷ։ൃɾվળ [ର৅ྖҬ]
 σʔλ࡞੒ɺը૾෼ੳɺࣗવݴޠॲཧ
 ର࿩ɺ৯จԽɺIoTσόΠεɺ։ൃج൫੔උ

5. ݚڀ։ൃ෦ͷऔΓ૊Έ 5

6. ݚڀ։ൃ෦ͷऔΓ૊Έɿը૾෼ੳ 6 ྉཧ/ඇྉཧ൑ఆ http://techlife.cookpad.com/entry/2017/09/14/161756
 http://techlife.cookpad.com/entry/2017/11/08/132538 ྉཧ/ඇྉཧ෼ྨɺϨγϐ෼ྨɺ௒ղ૾ɺϑΟϧλ࡞੒ɺͳͲ

7. ݚڀ։ൃ෦ͷऔΓ૊Έɿࣗવݴޠॲཧ 7 http://techlife.cookpad.com/entry/2015/09/30/170015
 http://techlife.cookpad.com/entry/2017/10/30/080102 MYϑΥϧμͷࣗಈ੔ཧɺࡐྉදهͷਖ਼نԽɺͳͲ

8. ݚڀ։ൃ෦ͷऔΓ૊ΈɿAmazon Echo ޲͚ͷΫοΫύουεΩϧ http://techlife.cookpad.com/entry/2017/11/21/181206
 http://techlife.cookpad.com/entry/2017/11/22/alexa-skilldesign

9. ݚڀ։ൃ෦ͷऔΓ૊ΈɿΠϯϑϥ؀ڥͱαʔϏε΁ͷܨ͗ࠐΈ 9 https://youtu.be/Jw9CpQkCvpM

10. ݚڀ։ൃ෦ͷऔΓ૊Έɿ৯จԽݚڀ 10 https://cookpad.com/kitchen/14604664
 https://info.cookpad.com/pr/news/press_2016_1208

11. ݚڀ։ൃ෦ͷऔΓ૊Έɿֶज़ํ໘΁ͷߩݙ 11 ɾ֤छֶձ΁ͷ࿦จ౤ߘ΍εϙϯαʔ
 ɹ IJCAI, SIGIR, JSAI, ALNP, IPSJ, CEA,

    XSIG2017, … ɾݚڀ༻ʹσʔληοτΛఏڙ
 ɹ https://www.nii.ac.jp/dsc/idr/cookpad/cookpad.html ɾίϯϖςΟγϣϯ༻ʹ໰୊ઃఆͱσʔληοτΛఏڙ
 ɹ- ਓ޻஌ೳٕज़ઓུձٞ౳ओ࠵ ୈ1ճAIνϟϨϯδίϯςετ
 ɹ https://deepanalytics.jp/compe/31 20170331ऴྃ
 ɹ- JSAI Cup 2018 ਓ޻஌ೳֶձσʔλղੳίϯϖςΟγϣϯ
 ɹ https://deepanalytics.jp/compe/59 20180329క੾

12. ࠓ೔͸ը૾෼ੳͷ࿩Λ͠·͢

13. ࣮ۀ຿ʹ͓͚Δը૾෼ੳͷࠔ೉ɿͦ΋ͦ΋ղ͚͍ͯΔ໰୊Ͱ͸ʁ 13 ෼ྨ໰୊͸ʮղ͚ͨʯ 0 7.5 15 22.5 30 2010 2011

    2012 2013 2014 2015 2016 2017 2.25 2.99 3.57 7.41 11.2 15.3 25.8 28.2 Classification error [%] Deep Learning !! human ability

14. ࣮ۀ຿ʹ͓͚Δը૾෼ੳͷࠔ೉ɿͦ΋ͦ΋ղ͚͍ͯΔ໰୊Ͱ͸ʁ 14 ෼ྨ໰୊͸ʮղ͚ͨʯ※ཧ૝తͳঢ়گԼͰ͸ ɾద੾ͳϥϕϧͷ෇༩
 ɹ Ұఆ౓Ҏ্ͷ࣭Ͱ֤ը૾ʹϥϕϧ͕෇༩͞Ε͍ͯΔ ɾద੾ͳΧςΰϦͷઃܭ
 ɹ ࢹ֮తʹ෼ྨͰ͖ΔΑ͏ͳΧςΰϦʹ෼͚ΒΕ͍ͯΔ ɾclosed

    set
 ɹ ֶशσʔλͷ෼෍ͱςετσʔλͷ෼෍͕౳͍͠

15. ࣮ۀ຿ʹ͓͚Δը૾෼ੳͷࠔ೉ɿͦ΋ͦ΋ղ͚͍ͯΔ໰୊Ͱ͸ʁ 15 ཧ૝ ≠ ݱ࣮ ɾద੾ͳϥϕϧͷ෇༩ɿ˚ ͋Δఔ౓σʔλྔͰΧόʔՄೳ ɾద੾ͳΧςΰϦͷઃܭɿ☓ {ϥʔϝϯ, ύελ,

    ΧϧϘφʔϥ} ͳͲ ɾclosed setɿ☓ ςετσʔλ͸ଟ༷Ͱ͔ͭಈత ͦ΋ͦ΋࣮αʔϏεͰղ͘΂͖໰୊͸ଟ͘ͷ৔߹ ”ؒҧ͍ͬͯΔ”
 → trial & error Ͱղ͘΂͖໰୊͕Կ͔Λ໌Β͔ʹ͍ͯ͘͠ͷ͕ओ୊

16. զʑ͕ͲͷΑ͏ʹͦΕΒͷ໰୊ʹऔΓ૊ΜͰ͍Δ͔ʁ ɾྉཧ͖Ζ͘Ͱͷػೳ
 ɹ Ϣʔβͷ࣋ͭը૾Λྉཧ/ඇྉཧ෼ྨ ɾϨγϐ෼ྨͰͷػೳ
 ɹ ྉཧࣸਅΛద੾ͳϨγϐʹ෼ྨ ɾྉཧ/ඇྉཧ෼ྨϞσϧͷϞόΠϧ࣮૷
 ɹ ϞσϧΛϞόΠϧʹҠ২ͯ͠ϓϥΠόγʔͷ໰୊ͳͲΛղܾ

    16 ۩ମతͳࣄྫͷ঺հ

17. ۩ମతͳࣄྫɿྉཧ͖Ζ͘Ͱͷྉཧ/ඇྉཧ෼ྨ ɾTechConf2017 Ͱ΋঺հ ɾྉཧͷࣸਅΛࣗಈతʹ෼ྨͯ͠දࣔɹ ɹ- CNNʹΑΔ෼ྨͰྉཧը૾Λநग़ ɹ- ৯ࣄͷৼΓฦΓ΍ͭ͘ΕΆͷଅਐ ɾ20180206࣌఺Ͱ ɹ-

    Ϣʔβ਺ɿ19ສਓҎ্ ɹ- ྦྷੵྉཧຕ਺ɿ1900ສຕҎ্ 17 ྉཧ͖Ζ͘ͷਐԽͱݱࡏ https://speakerdeck.com/ayemos/real-world-machine-learning

18. ۩ମతͳࣄྫɿྉཧ͖Ζ͘Ͱͷྉཧ/ඇྉཧ෼ྨ 18 ػցֶशͷ؍఺͔Βॏཁͳ఺ ɾΫΠοΫελʔτ ɹը૾෼ੳͷ஌ݟ͕ෆे෼ͳͱ͖͔Β CaffeNet Ͱૉૣ࣮͘૷ ɾϞσϧͷվળͱۤखͳΧςΰϦͷߟྀ ɹ Inception

    V3 ͷ࢖༻΍ multi-class Ϟσϧͷ࢖༻ ɾςετσʔλͷ֦ॆ ɹࣾһ͔ΒσʔλΛूΊ࣮ͯ؀ڥʹ͍ۙঢ়گͰݕূ ɾہॴੑΛऔΓࠐΉͨΊͷύονԽ ɹࣸਅͷҰ෦ʹྉཧ͕͍ࣸͬͯΔঢ়گʹదԠ http://techlife.cookpad.com/entry/2017/09/14/161756
 http://techlife.cookpad.com/entry/2017/11/08/132538

19. ۩ମతͳࣄྫɿྉཧ͖Ζ͘Ͱͷྉཧ/ඇྉཧ෼ྨ 19 ɾہॴੑΛऔΓࠐΉͨΊͷύονԽ ɹ- ෦෼తͳྉཧը૾Λर͍͍ͨʢsegmentation ·Ͱ͸͍Βͳ͍ʣ ɹ- ը૾Λύονʹ෼͚ͯͦΕͧΕͰ෼ྨ͢ΔϞσϧΛߏங

20. ۩ମతͳࣄྫɿྉཧࣸਅͷϨγϐΧςΰϦ෼ྨ ɾྉཧࣸਅΛద੾ͳϨγϐΧςΰϦʹ෼ྨ ɾ୯७ͳ෼ྨʹݟ࣮͑ͯ͸ඇৗʹ೉͍͠ ɹ- open set ʹ͓͚Δ༧ଌ ɹ- ༧ଌର৅ͷΧςΰϦͷઃܭ ɹ-

    ྨࣅΧςΰϦͷଘࡏ ɾ༷ʑͳ࣮ݧΛܦͯϞσϧΛ࡞੒ ɹ- ྨࣅΧςΰϦͷ෼ྨͱ precision ʹ஫ྗ 20 ྉཧ͖Ζ͘ͷͦͷઌ΁

21. ۩ମతͳࣄྫɿྉཧࣸਅͷϨγϐΧςΰϦ෼ྨ 21 ػցֶशͷ؍఺͔Βॏཁͳ఺ ɾ෼ྨͷର৅ͱͳΔΧςΰϦͷઃܭ ɹαʔϏεͱ݉Ͷ߹͍ΛਤΓͭͭ༧ଌର৅ΧςΰϦΛબఆ ɾྨࣅΧςΰϦʹର͢Δ෼ྨ ɹ ΧςΰϦؒͷྨࣅ౓͕େ͖͘ҟͳΔͷͰఆྔతͳධՁ๏ΛߟҊ ɾopen set

    ͳ෼ྨ໰୊ʹ͓͚Δ precision ͷ֬อ ɹOne vs. Rest ෼ྨثΛ૊Έ߹Θͤͯ precision ΛߴΊΔΑ͏ௐ੔ ɾධՁํ๏ͷઃܭ ɹΦϯϥΠϯͰ͸ϑΟʔυόοΫɺΦϑϥΠϯͰ͸σʔλ࡞੒ ࿦จ : https://arxiv.org/abs/1802.01267

22. ۩ମతͳࣄྫɿྉཧࣸਅͷϨγϐΧςΰϦ෼ྨ 22 ɾΧςΰϦߏ଄ͱΧςΰϦؒྨࣅ౓ͷఆࣜԽ ɹ- ੜ੒Ϟσϧͷ؍఺ɺϥϕϧ෇͚ͷ֬཰ੑɺ༧ଌϥϕϧͱͷؔ܎ ɹ- ֶशϞσϧͷ ”ޡ෼ྨ” ͔ΒΧςΰϦؒྨࣅ౓Λఆٛ

23. ۩ମతͳࣄྫɿྉཧࣸਅͷϨγϐΧςΰϦ෼ྨ 23 ɾ࣮ࡍͷΧςΰϦઃܭͷεςοϓ ɹ- ϝλσʔλ͔ΒશΧςΰϦΛநग़ʢશ෦Ͱ1,000ΧςΰϦఔ౓ʣ ↓ ɹ- ࢹ֮తͰͳ͍΋ͷ΍਺͕গͳ͍΋ͷΛআ֎ʢେࡼྉཧͳͲʣ ↓ ɹ-

    αʔϏεʹ͓͍ͯ༗༻ͦ͏ͳ΋ͷΛਓྗͰநग़ʢ͜͜ॏཁʣ ↓ ɹ- ޡ෼ྨʹجͮ͘ྨࣅ౓Ͱ౷ഇ߹ʢ࠷ऴతʹ50ΧςΰϦఔ౓ʣ ྫʣ΍͖ͦ͹ͱϏʔϑϯΛಉ͡ΧςΰϦͱͯ͠౷߹

24. ۩ମతͳࣄྫɿྉཧࣸਅͷϨγϐΧςΰϦ෼ྨ 24 ɾprecision ΛߴΊΔͨΊʹ One vs. Rest ෼ྨثʹΑΔϞσϧΛߏங ɹ- ར఺ɿݸʑͷΧςΰϦʹ߹Θͤͨॊೈͳઃܭ͕Մೳ

    ɹ- ܽ఺ɿॱ൪΍ᮢ஋ͳͲ hand crafted ͳ෦෼΋গͳ͘ͳ͍ feature extractor for c in {αϥμ, ύελ, …} 0 1 ྉཧը૾Ͱ
 pre-train ͨ͠
 Inception V3 1 0 αϥμ next next f2 ͸ྨࣅ౓͕ߴ͍ΧςΰϦ ͚ͩΛूΊֶͯशͨ͠ One vs. Rest ෼ྨث

25. ۩ମతͳࣄྫɿը૾෼ྨϞσϧͷϞόΠϧ΁ͷҠ২ ɾղܾ͍ͨ͠໰୊͸·ͩ·ͩ͋Δ ɹ- ଈ࣌ੑɿࡱͬͨࣸਅ͕Ͱ͖Δ͚ͩૣ͘൓ө͞Εͯཉ͍͠ ɹ- ػີੑɿϢʔβͷࣸਅ͸ݟ͍ͯͳ͍͕৺ཧత఍߅͸࢒Δ ɹ- ֦େੑɿܭࢉࢿݯΛ؆୯ʹεέʔϧ͍ͤͨ͞ ɹ- Ԡ༻ੑɿΞϓϦ಺Ͱ෼ྨ༷ͯ͠ʑͳαʔϏεʹԠ༻͍ͨ͠

    ɾϞόΠϧ࣮૷ͷػӡ ɹ - ܰྔͰߴੑೳͳϞσϧ͕֤छଘࡏ ʢSqueezeNet ΍ MobileNetʣ ɹ - ֤छϥΠϒϥϦͷॆ࣮ʢCore ML ΍ TensorFlow Liteʣ 25 ϞόΠϧ΁ͷҠߦ

26. ۩ମతͳࣄྫɿը૾෼ྨϞσϧͷϞόΠϧ΁ͷҠ২ 26 ػցֶशͷ؍఺͔Βॏཁͳ఺ ɾਫ਼౓Λग़དྷΔݶΓམͱͣܰ͞ྔͳϞσϧΛ࡞Δ ɹܰྔԽΛ໨తͱͨ͠ߏ଄΍ྔࢠԽͳͲͷཧղ ɾϞόΠϧଆͱͷ࿈ܞ ɹ iOS ΍ Android

    ଆͷ஌ݟ͕ෆՄܽ ɾ৘ใ͕গͳ͍தͰͷϓϩδΣΫτਪਐ ɹ ػցֶशͱϞόΠϧͷͦΕͧΕͷྖҬͰਂ͍ཧղ͕ॏཁ ɾϥΠϒϥϦͷόʔδϣϯґଘੑͳͲΛద੾ʹѻ͏ ɹྫʣcoremltools ͸ 201802 ·Ͱ͸ python 2.7 ܥͰͷΈར༻Մ

27. ۩ମతͳࣄྫɿը૾෼ྨϞσϧͷϞόΠϧ΁ͷҠ২ 27 ɾྉཧ/ඇྉཧ෼ྨϞσϧΛϞόΠϧʹҠ২ ɹ- MobileNet ͱہॴԽͷͨΊͷύονԽΛ߹Θͤͨߏ଄ ɹ- αʔό্ͷ࣮ݧʢը૾20,000ຕఔ౓ʣͰ 1% ఔ౓ͷਖ਼౴཰ͷࠩ

    ɹ- iOS, Android ڞʹ࣮ػͰݕূ͓ͯ͠Γಉఔ౓ͷੑೳ ɾBristol ΦϑΟε΁ͷग़ு࣌ʹਐΊͨϓϩδΣΫτ ɹ- iOS, Android ΤϯδχΞʹڠྗͯ͠΋Β͍ҰؾʹਐΜͩ ɹ- ࠃ಺֎ͰਐΊ͍͚ͯͦ͏ͳτϐοΫ

28. ۩ମతͳࣄྫɿը૾෼ྨϞσϧͷϞόΠϧ΁ͷҠ২ 28 ɾAndroid (Pixel 2 at Bristol) Ͱͷ࣮ݧ݁Ռ Original Quantized

    Model Size 12 [MB] 3.3 [MB] Accuracy 0.97 0.97 Precision 0.98 0.98 Recall 0.96 0.96 CPU Usage 40-60 [%] 40-60 [%] Memory Usage 120 [MB] 90 [MB] FPS 7.54 [FPS] 7.72 [FPS] DEMO

29. ը૾෼ੳͷίϯϖ΍ͬͯ·͢ʂ JSAI Cup 2018 క੾ : 20180329 https://deepanalytics.jp/compe/59

30. ·ͱΊ 30 ໰୊Λఆٛ͠ɺͦΕΛਵ࣌ߋ৽ͯ͠ղ͍͍ͯ͘ ɾ࣮ۀ຿Ͱͷը૾෼ੳͷ໰୊͸·ͩ·ͩ “ղ͚ͯͳ͍“
 ɹ ਖ਼֬ʹ͸ղ͘΂͖໰୊͕໌֬ʹఆٛͰ͖͍ͯΔ͜ͱ͕গͳ͍ ɾࢼߦࡨޡͷ຤ʹͦΕΛݱঢ়ͷٕज़Ͱղ͚Δ໰୊ʹམͱ͠ࠐΉ
 ɹ ը૾෼ੳͷཁૉٕज़͸੒ख़͖͍ͯͯͯ͜͠Ε͕ॏཁͳϑΣʔζ


    ɹ ΫοΫύουͰ͸ྉཧ͖Ζ͘΍Ϩγϐ෼ྨʹը૾෼ੳΛಋೖ ɾϞόΠϧ΁ͷҠ২΍ಈըͳͲ͕ը૾෼ੳͷ࣍ͷ໘നͦ͏ͳྖҬ

31. ࠷ޙʹɿΫοΫύου͸ʢগͳ͘ͱ΋ࣗ෼ʹͱͬͯ͸ʣಇ͖΍͍͢ 31 ݚڀ։ൃ෦Ͱಇ͘͜ͱ = ྑήʔ ɾྑετʔϦʔ
 ɹʮຖ೔ͷྉཧΛָ͠Έʹ͢Δʯͱ͍͏ϛογϣϯͷԼͰڠಇ ɾߴࣗ༝౓
 ɹ৽͍͠௅ઓʹॏ͖Λஔ͍͍ͯͯ trial

    & error Λਪ঑ ɾָγεςϜ
 ɹैۀһ͕ಇ͖΍͘͢ύϑΥʔϚϯεΛग़͠΍͍͢؀ڥ

32. [એ఻] ਓ޻஌ೳֶձओ࠵ͷNIPS2018ใࠂձͰൃද͠·͢ 32 https://www.ai-gakkai.or.jp/no74_jsai_seminar/