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20180210_Cookpad_TechConf2018_YoheiKIKUTA
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yoppe
February 10, 2018
Technology
5
1.2k
20180210_Cookpad_TechConf2018_YoheiKIKUTA
Talk at Cookpad TechConf 2018 (
https://techconf.cookpad.com/2018/
).
yoppe
February 10, 2018
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Transcript
٠ా ངฏ ݚڀ։ൃ෦ Solve “unsolved” image recognition problems in service
applications Cookpad Inc. Feb 10th, 2018
ࣗݾհ → https://github.com/yoheikikuta/resume ɾ໊લɿ٠ా ངฏ @yohei_kikuta ɾॴଐɿݚڀ։ൃ෦ ɾݞॻɿϦαʔνΤϯδχΞ ɹɹɹɹത࢜ʢཧֶʣ ɾઐɿը૾ੳ
ɾɿম͖ᰤࢠɺण࢘ɺDr Pepper 2
࣍ 3 ɾݚڀ։ൃ෦ͷհ ɾ࣮ۀʹ͓͚Δը૾ੳͷࠔ ɾΫοΫύουͰ۩ମతʹը૾ੳʹऔΓΜͰ͍Δࣄྫͷհ - ྉཧ͖Ζ͘ɿҙͷը૾ͷྉཧ/ඇྉཧྨ - Ϩγϐྨɿྉཧը૾ͷϨγϐΧςΰϦྨ -
ϞόΠϧ࣮ɿϞόΠϧͰಈ͘ྉཧը૾ྨͷϞσϧߏங ɾ·ͱΊ
ݚڀ։ൃ෦ͷϝϯόʔ 4 ৽نٕज़Λ׆༻ͨ͠αʔϏεͷ։ൃɾվળ [ରྖҬ] σʔλ࡞ɺը૾ੳɺࣗવݴޠॲཧ ରɺ৯จԽɺIoTσόΠεɺ։ൃج൫උ
ݚڀ։ൃ෦ͷऔΓΈ 5
ݚڀ։ൃ෦ͷऔΓΈɿը૾ੳ 6 ྉཧ/ඇྉཧఆ http://techlife.cookpad.com/entry/2017/09/14/161756 http://techlife.cookpad.com/entry/2017/11/08/132538 ྉཧ/ඇྉཧྨɺϨγϐྨɺղ૾ɺϑΟϧλ࡞ɺͳͲ
ݚڀ։ൃ෦ͷऔΓΈɿࣗવݴޠॲཧ 7 http://techlife.cookpad.com/entry/2015/09/30/170015 http://techlife.cookpad.com/entry/2017/10/30/080102 MYϑΥϧμͷࣗಈཧɺࡐྉදهͷਖ਼نԽɺͳͲ
ݚڀ։ൃ෦ͷऔΓΈɿAmazon Echo ͚ͷΫοΫύουεΩϧ http://techlife.cookpad.com/entry/2017/11/21/181206 http://techlife.cookpad.com/entry/2017/11/22/alexa-skilldesign
ݚڀ։ൃ෦ͷऔΓΈɿΠϯϑϥڥͱαʔϏεͷܨ͗ࠐΈ 9 https://youtu.be/Jw9CpQkCvpM
ݚڀ։ൃ෦ͷऔΓΈɿ৯จԽݚڀ 10 https://cookpad.com/kitchen/14604664 https://info.cookpad.com/pr/news/press_2016_1208
ݚڀ։ൃ෦ͷऔΓΈɿֶज़ํ໘ͷߩݙ 11 ɾ֤छֶձͷจߘεϙϯαʔ ɹ IJCAI, SIGIR, JSAI, ALNP, IPSJ, CEA,
XSIG2017, … ɾݚڀ༻ʹσʔληοτΛఏڙ ɹ https://www.nii.ac.jp/dsc/idr/cookpad/cookpad.html ɾίϯϖςΟγϣϯ༻ʹઃఆͱσʔληοτΛఏڙ ɹ- ਓೳٕज़ઓུձٞओ࠵ ୈ1ճAIνϟϨϯδίϯςετ ɹ https://deepanalytics.jp/compe/31 20170331ऴྃ ɹ- JSAI Cup 2018 ਓೳֶձσʔλղੳίϯϖςΟγϣϯ ɹ https://deepanalytics.jp/compe/59 20180329క
ࠓը૾ੳͷΛ͠·͢
࣮ۀʹ͓͚Δը૾ੳͷࠔɿͦͦղ͚͍ͯΔͰʁ 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
࣮ۀʹ͓͚Δը૾ੳͷࠔɿͦͦղ͚͍ͯΔͰʁ 14 ྨʮղ͚ͨʯ※ཧతͳঢ়گԼͰ ɾదͳϥϕϧͷ༩ ɹ ҰఆҎ্ͷ࣭Ͱ֤ը૾ʹϥϕϧ͕༩͞Ε͍ͯΔ ɾదͳΧςΰϦͷઃܭ ɹ ࢹ֮తʹྨͰ͖ΔΑ͏ͳΧςΰϦʹ͚ΒΕ͍ͯΔ ɾclosed
set ɹ ֶशσʔλͷͱςετσʔλͷ͕͍͠
࣮ۀʹ͓͚Δը૾ੳͷࠔɿͦͦղ͚͍ͯΔͰʁ 15 ཧ ≠ ݱ࣮ ɾదͳϥϕϧͷ༩ɿ˚ ͋ΔఔσʔλྔͰΧόʔՄೳ ɾదͳΧςΰϦͷઃܭɿ☓ {ϥʔϝϯ, ύελ,
ΧϧϘφʔϥ} ͳͲ ɾclosed setɿ☓ ςετσʔλଟ༷Ͱ͔ͭಈత ࣮ͦͦαʔϏεͰղ͖͘ଟ͘ͷ߹ ”ؒҧ͍ͬͯΔ” → trial & error Ͱղ͖͕͘Կ͔Λ໌Β͔ʹ͍ͯ͘͠ͷ͕ओ
զʑ͕ͲͷΑ͏ʹͦΕΒͷʹऔΓΜͰ͍Δ͔ʁ ɾྉཧ͖Ζ͘Ͱͷػೳ ɹ Ϣʔβͷ࣋ͭը૾Λྉཧ/ඇྉཧྨ ɾϨγϐྨͰͷػೳ ɹ ྉཧࣸਅΛదͳϨγϐʹྨ ɾྉཧ/ඇྉཧྨϞσϧͷϞόΠϧ࣮ ɹ ϞσϧΛϞόΠϧʹҠ২ͯ͠ϓϥΠόγʔͷͳͲΛղܾ
16 ۩ମతͳࣄྫͷհ
۩ମతͳࣄྫɿྉཧ͖Ζ͘Ͱͷྉཧ/ඇྉཧྨ ɾTechConf2017 Ͱհ ɾྉཧͷࣸਅΛࣗಈతʹྨͯ͠දࣔɹ ɹ- CNNʹΑΔྨͰྉཧը૾Λநग़ ɹ- ৯ࣄͷৼΓฦΓͭ͘ΕΆͷଅਐ ɾ20180206࣌Ͱ ɹ-
Ϣʔβɿ19ສਓҎ্ ɹ- ྦྷੵྉཧຕɿ1900ສຕҎ্ 17 ྉཧ͖Ζ͘ͷਐԽͱݱࡏ https://speakerdeck.com/ayemos/real-world-machine-learning
۩ମతͳࣄྫɿྉཧ͖Ζ͘Ͱͷྉཧ/ඇྉཧྨ 18 ػցֶशͷ؍͔Βॏཁͳ ɾΫΠοΫελʔτ ɹը૾ੳͷݟ͕ෆेͳͱ͖͔Β CaffeNet Ͱૉૣ࣮͘ ɾϞσϧͷվળͱۤखͳΧςΰϦͷߟྀ ɹ Inception
V3 ͷ༻ multi-class Ϟσϧͷ༻ ɾςετσʔλͷ֦ॆ ɹࣾһ͔ΒσʔλΛूΊ࣮ͯڥʹ͍ۙঢ়گͰݕূ ɾہॴੑΛऔΓࠐΉͨΊͷύονԽ ɹࣸਅͷҰ෦ʹྉཧ͕͍ࣸͬͯΔঢ়گʹదԠ http://techlife.cookpad.com/entry/2017/09/14/161756 http://techlife.cookpad.com/entry/2017/11/08/132538
۩ମతͳࣄྫɿྉཧ͖Ζ͘Ͱͷྉཧ/ඇྉཧྨ 19 ɾہॴੑΛऔΓࠐΉͨΊͷύονԽ ɹ- ෦తͳྉཧը૾Λर͍͍ͨʢsegmentation ·Ͱ͍Βͳ͍ʣ ɹ- ը૾Λύονʹ͚ͯͦΕͧΕͰྨ͢ΔϞσϧΛߏங
۩ମతͳࣄྫɿྉཧࣸਅͷϨγϐΧςΰϦྨ ɾྉཧࣸਅΛదͳϨγϐΧςΰϦʹྨ ɾ୯७ͳྨʹݟ࣮͑ͯඇৗʹ͍͠ ɹ- open set ʹ͓͚Δ༧ଌ ɹ- ༧ଌରͷΧςΰϦͷઃܭ ɹ-
ྨࣅΧςΰϦͷଘࡏ ɾ༷ʑͳ࣮ݧΛܦͯϞσϧΛ࡞ ɹ- ྨࣅΧςΰϦͷྨͱ precision ʹྗ 20 ྉཧ͖Ζ͘ͷͦͷઌ
۩ମతͳࣄྫɿྉཧࣸਅͷϨγϐΧςΰϦྨ 21 ػցֶशͷ؍͔Βॏཁͳ ɾྨͷରͱͳΔΧςΰϦͷઃܭ ɹαʔϏεͱ݉Ͷ߹͍ΛਤΓͭͭ༧ଌରΧςΰϦΛબఆ ɾྨࣅΧςΰϦʹର͢Δྨ ɹ ΧςΰϦؒͷྨࣅ͕େ͖͘ҟͳΔͷͰఆྔతͳධՁ๏ΛߟҊ ɾopen set
ͳྨʹ͓͚Δ precision ͷ֬อ ɹOne vs. Rest ྨثΛΈ߹Θͤͯ precision ΛߴΊΔΑ͏ௐ ɾධՁํ๏ͷઃܭ ɹΦϯϥΠϯͰϑΟʔυόοΫɺΦϑϥΠϯͰσʔλ࡞ จ : https://arxiv.org/abs/1802.01267
۩ମతͳࣄྫɿྉཧࣸਅͷϨγϐΧςΰϦྨ 22 ɾΧςΰϦߏͱΧςΰϦؒྨࣅͷఆࣜԽ ɹ- ੜϞσϧͷ؍ɺϥϕϧ͚ͷ֬ੑɺ༧ଌϥϕϧͱͷؔ ɹ- ֶशϞσϧͷ ”ޡྨ” ͔ΒΧςΰϦؒྨࣅΛఆٛ
۩ମతͳࣄྫɿྉཧࣸਅͷϨγϐΧςΰϦྨ 23 ɾ࣮ࡍͷΧςΰϦઃܭͷεςοϓ ɹ- ϝλσʔλ͔ΒશΧςΰϦΛநग़ʢશ෦Ͱ1,000ΧςΰϦఔʣ ↓ ɹ- ࢹ֮తͰͳ͍ͷ͕গͳ͍ͷΛআ֎ʢେࡼྉཧͳͲʣ ↓ ɹ-
αʔϏεʹ͓͍ͯ༗༻ͦ͏ͳͷΛਓྗͰநग़ʢ͜͜ॏཁʣ ↓ ɹ- ޡྨʹجͮ͘ྨࣅͰ౷ഇ߹ʢ࠷ऴతʹ50ΧςΰϦఔʣ ྫʣ͖ͦͱϏʔϑϯΛಉ͡ΧςΰϦͱͯ͠౷߹
۩ମతͳࣄྫɿྉཧࣸਅͷϨγϐΧςΰϦྨ 24 ɾprecision ΛߴΊΔͨΊʹ One vs. Rest ྨثʹΑΔϞσϧΛߏங ɹ- རɿݸʑͷΧςΰϦʹ߹Θͤͨॊೈͳઃܭ͕Մೳ
ɹ- ܽɿॱ൪ᮢͳͲ hand crafted ͳ෦গͳ͘ͳ͍ feature extractor for c in {αϥμ, ύελ, …} 0 1 ྉཧը૾Ͱ pre-train ͨ͠ Inception V3 1 0 αϥμ next next f2 ྨࣅ͕ߴ͍ΧςΰϦ ͚ͩΛूΊֶͯशͨ͠ One vs. Rest ྨث
۩ମతͳࣄྫɿը૾ྨϞσϧͷϞόΠϧͷҠ২ ɾղܾ͍ͨ͠·ͩ·ͩ͋Δ ɹ- ଈ࣌ੑɿࡱͬͨࣸਅ͕Ͱ͖Δ͚ͩૣ͘ө͞Εͯཉ͍͠ ɹ- ػີੑɿϢʔβͷࣸਅݟ͍ͯͳ͍͕৺ཧత߅Δ ɹ- ֦େੑɿܭࢉࢿݯΛ؆୯ʹεέʔϧ͍ͤͨ͞ ɹ- Ԡ༻ੑɿΞϓϦͰྨ༷ͯ͠ʑͳαʔϏεʹԠ༻͍ͨ͠
ɾϞόΠϧ࣮ͷػӡ ɹ - ܰྔͰߴੑೳͳϞσϧ͕֤छଘࡏ ʢSqueezeNet MobileNetʣ ɹ - ֤छϥΠϒϥϦͷॆ࣮ʢCore ML TensorFlow Liteʣ 25 ϞόΠϧͷҠߦ
۩ମతͳࣄྫɿը૾ྨϞσϧͷϞόΠϧͷҠ২ 26 ػցֶशͷ؍͔Βॏཁͳ ɾਫ਼Λग़དྷΔݶΓམͱͣܰ͞ྔͳϞσϧΛ࡞Δ ɹܰྔԽΛతͱͨ͠ߏྔࢠԽͳͲͷཧղ ɾϞόΠϧଆͱͷ࿈ܞ ɹ iOS Android
ଆͷݟ͕ෆՄܽ ɾใ͕গͳ͍தͰͷϓϩδΣΫτਪਐ ɹ ػցֶशͱϞόΠϧͷͦΕͧΕͷྖҬͰਂ͍ཧղ͕ॏཁ ɾϥΠϒϥϦͷόʔδϣϯґଘੑͳͲΛదʹѻ͏ ɹྫʣcoremltools 201802 ·Ͱ python 2.7 ܥͰͷΈར༻Մ
۩ମతͳࣄྫɿը૾ྨϞσϧͷϞόΠϧͷҠ২ 27 ɾྉཧ/ඇྉཧྨϞσϧΛϞόΠϧʹҠ২ ɹ- MobileNet ͱہॴԽͷͨΊͷύονԽΛ߹Θͤͨߏ ɹ- αʔό্ͷ࣮ݧʢը૾20,000ຕఔʣͰ 1% ఔͷਖ਼ͷࠩ
ɹ- iOS, Android ڞʹ࣮ػͰݕূ͓ͯ͠Γಉఔͷੑೳ ɾBristol ΦϑΟεͷग़ு࣌ʹਐΊͨϓϩδΣΫτ ɹ- iOS, Android ΤϯδχΞʹڠྗͯ͠Β͍ҰؾʹਐΜͩ ɹ- ࠃ֎ͰਐΊ͍͚ͯͦ͏ͳτϐοΫ
۩ମతͳࣄྫɿը૾ྨϞσϧͷϞόΠϧͷҠ২ 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
ը૾ੳͷίϯϖͬͯ·͢ʂ JSAI Cup 2018 క : 20180329 https://deepanalytics.jp/compe/59
·ͱΊ 30 Λఆٛ͠ɺͦΕΛਵ࣌ߋ৽ͯ͠ղ͍͍ͯ͘ ɾ࣮ۀͰͷը૾ੳͷ·ͩ·ͩ “ղ͚ͯͳ͍“ ɹ ਖ਼֬ʹղ͖͕͘໌֬ʹఆٛͰ͖͍ͯΔ͜ͱ͕গͳ͍ ɾࢼߦࡨޡͷʹͦΕΛݱঢ়ͷٕज़Ͱղ͚Δʹམͱ͠ࠐΉ ɹ ը૾ੳͷཁૉٕज़ख़͖͍ͯͯͯ͜͠Ε͕ॏཁͳϑΣʔζ
ɹ ΫοΫύουͰྉཧ͖Ζ͘Ϩγϐྨʹը૾ੳΛಋೖ ɾϞόΠϧͷҠ২ಈըͳͲ͕ը૾ੳͷ࣍ͷ໘നͦ͏ͳྖҬ
࠷ޙʹɿΫοΫύουʢগͳ͘ͱࣗʹͱͬͯʣಇ͖͍͢ 31 ݚڀ։ൃ෦Ͱಇ͘͜ͱ = ྑήʔ ɾྑετʔϦʔ ɹʮຖͷྉཧΛָ͠Έʹ͢Δʯͱ͍͏ϛογϣϯͷԼͰڠಇ ɾߴࣗ༝ ɹ৽͍͠ઓʹॏ͖Λஔ͍͍ͯͯ trial
& error Λਪ ɾָγεςϜ ɹैۀһ͕ಇ͖͘͢ύϑΥʔϚϯεΛग़͍͢͠ڥ
[એ] ਓೳֶձओ࠵ͷNIPS2018ใࠂձͰൃද͠·͢ 32 https://www.ai-gakkai.or.jp/no74_jsai_seminar/