Upgrade to PRO for Only $50/Year—Limited-Time Offer! 🔥
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Caffeでお手軽本格ディープラーニングiOSアプリ
Search
Takuya Matsuyama
October 13, 2015
Technology
1
1.6k
Caffeでお手軽本格ディープラーニングiOSアプリ
@potatotips #22
#DeepLearning #MachineLearning
Takuya Matsuyama
October 13, 2015
Tweet
Share
More Decks by Takuya Matsuyama
See All by Takuya Matsuyama
ネイティブモジュールの作り方 @ React Native Meetup #9 in Japan
craftzdog
6
1.3k
How to Create Native Modules @ React Native Japan Meetup #9
craftzdog
1
880
Introducing Inkdrop for Mobile Built with React Native
craftzdog
1
2.2k
The fun Deep Learning
craftzdog
0
2.9k
Other Decks in Technology
See All in Technology
32のキーワードで学ぶ はじめての耐量子暗号(PQC) / Getting Started with Post-Quantum Cryptography in 32 keywords
quiver
0
200
日本Rubyの会の構造と実行とあと何か / hokurikurk01
takahashim
2
400
AI/MLのマルチテナント基盤を支えるコンテナ技術
pfn
PRO
5
720
モバイルゲーム開発におけるエージェント技術活用への試行錯誤 ~開発効率化へのアプローチの紹介と未来に向けた展望~
qualiarts
0
280
.NET 10 のパフォーマンス改善
nenonaninu
2
4.7k
Uncertainty in the LLM era - Science, more than scale
gaelvaroquaux
0
480
Product Engineer
resilire
0
130
シンプルを極める。アンチパターンなDB設計の本質
facilo_inc
1
1k
20251127 BigQueryリモート関数で作る、お手軽AIバッチ実行環境
daimatz
0
430
プロダクトマネージャーが押さえておくべき、ソフトウェア資産とAIエージェント投資効果 / pmconf2025
i35_267
2
340
HIG学習用スライド
yuukiw00w
0
110
研究開発部メンバーの働き⽅ / Sansan R&D Profile
sansan33
PRO
3
21k
Featured
See All Featured
How Fast Is Fast Enough? [PerfNow 2025]
tammyeverts
3
380
Scaling GitHub
holman
464
140k
Fantastic passwords and where to find them - at NoRuKo
philnash
52
3.5k
Mobile First: as difficult as doing things right
swwweet
225
10k
Side Projects
sachag
455
43k
Statistics for Hackers
jakevdp
799
230k
Product Roadmaps are Hard
iamctodd
PRO
55
12k
Creating an realtime collaboration tool: Agile Flush - .NET Oxford
marcduiker
35
2.3k
[RailsConf 2023] Rails as a piece of cake
palkan
58
6.1k
Visualizing Your Data: Incorporating Mongo into Loggly Infrastructure
mongodb
48
9.8k
JavaScript: Past, Present, and Future - NDC Porto 2020
reverentgeek
52
5.7k
Faster Mobile Websites
deanohume
310
31k
Transcript
$B⒎FͰ͓खܰຊ֨ σΟʔϓϥʔχϯά J04ΞϓϦ 5",6:" !OPSBEBJLP QPUBUPUJQT
দࢁ w !OPSBEBJLP w ϑϦʔϥϯε ݩ:BIPP w J04ΞϓϦ ΣϒΞϓϦͳͲΛ੍࡞
w ػցֶशʹڵຯ͋Γ w ֆඳ͖·͢ 2
ΊΜ͖͖ ໙ར͖ 3
4 ໙ར͖ ࣸਅʹج͍ͮͯϥʔϝϯΛਪન͢ΔΞϓϦ ೖྗ
5 σΟʔϓϥʔχϯά ͷٕज़Λ༻ ʴ ʹ
ը૾ೝࣝʹڧ͍ ػցֶशΞϧΰϦζϜ 6 σΟʔϓϥʔχϯάͱ
w ͷਆܦߏΛ฿ͨ͠χϡʔϥϧωοτϫʔΫͷҰछ w େྔͷσʔλ͔ΒମͷಛΛࣗಈతʹֶश ‣ ͜Ε·Ͱಛͷநग़ํ๏ਓ͕͕ؒΜͬͯ༻ҙ͍ͯͨ͠ 7
࡞Ζ͏ σΟʔϓϥʔχϯάΞϓϦ ୭Ͱ؆୯ʹ࡞ΕΔํ๏Λ͝հ͠·͢ 8
$B⒎F σΟʔϓϥʔχϯά༻ ϑϨʔϜϫʔΫ w IUUQDB⒎FCFSLFMFZWJTJPOPSH w (16ԋࢉ $6%" ͰߴʹֶशͰ͖Δ w
͙͢ʹࢼͤΔֶशࡁΈϞσϧ͋Δ w .BD049ରԠ 9
Caffe for J04্Ͱಈ͘$B⒎F w IUUQTHJUIVCDPNBMFQIDB⒎F w $B⒎FͷGPSL w J04্Ͱࣝผॲཧ͕࣮༻ʹ͑ΔͰಈ͔ͤΔ ‣
J1IPOFTͰʙඵ w αʔό͍ΒͣͰ͑Δ w ͨͩ͠9$PEF·ͩඇରԠ 10
$B⒎FGPSJ04 αϯϓϧ࡞Γ·ͨ͠ w IUUQTHJUIVCDPNOPSBEBJLP DB⒎FJPTTBNQMF w ୯७ͳମೝࣝ w #-7$$B⒎F/FU.PEFMΛ༻ 11
demo
༻͢Δσʔλ w MBCFMTUYUࣝผ݁ՌΛ໊લʹม͢ΔͨΊͷҰཡ w EFQMPZQSPUPUYUωοτϫʔΫఆٛ w NFBOCJOBSZQSPUPฏۉը૾ w CWMD@SFGFSFODF@DB⒎FOFUDB⒎FNPEFMֶशࡁΈσʔλ 13
ॲཧͷྲྀΕ ࣝผରͷը૾ͷಡΈࠐΈ w ૾ͷը૾ $MBTTJpFSΫϥεͷॳظԽ w ͭͷϞσϧσʔλͷϑΝΠϧύεΛࢦఆ $MBTTJpFSͷ࣮ߦ w ը૾Λࢦఆͯ݁͠ՌΛऔಘ
ࣝผ݁Ռͷग़ྗ 14
UIImage* image = [UIImage imageNamed:@"sample.jpg"]; cv::Mat src_img, img; UIImageToMat(image, src_img);
cv::cvtColor(src_img, img, CV_RGBA2BGRA); ը૾ͷಡΈࠐΈ w 6**NBHFΛಡΈࠐΈ w DW.BUܗࣜʹม w ΧϥʔྻΛ3(#"͔Β#(3"ʹม
// ϑΝΠϧύεΛstringܕʹม string model_file_str = std::string([model_file UTF8String]); string label_file_str =
std::string([label_file UTF8String]); string trained_file_str = std::string([trained_file UTF8String]); string mean_file_str = std::string([mean_file UTF8String]); Classifier classifier = Classifier(model_file_str, trained_file_str, mean_file_str, label_file_str); $MBTTJpFSͷॳظԽ w ϞσϧఆٛɺϥϕϧɺֶशࡁΈϞσϧɺฏۉը૾ͷύεΛऔಘ w ֤ϑΝΠϧύεΛTUETUSJOHʹม w $MBTTJpFSͷΠϯελϯεΛ࡞
// ࣝผͷ࣮ߦ std::vector<Prediction> result = classifier.Classify(img); $MBTTJpFSͷ࣮ߦ w ը૾Λࢦఆ͢Δ͚ͩʂ
for (std::vector<Prediction>::iterator it = result.begin(); it != result.end(); ++it) {
NSString* label = [NSString stringWithUTF8String:it->first.c_str()]; NSNumber* probability = [NSNumber numberWithFloat:it->second]; NSLog(@"label: %@, prob: %@", label, probability); } ࣝผ݁Ռͷग़ྗ w TUEWFDUPSܗࣜͰෳͷࣝผީิ͕ಘΒΕΔ w JUFSBUPSͰճ֤ͯ͠ީิΛऔಘ w JUpSTUϥϕϧɺJUTFDPOE֬
·ͱΊ w $B⒎FΛ͑ΦϦδφϧͷֶशϞσϧ͕࡞ΕΔ w $B⒎FGPSJ04ͳΒαʔό͍ΒͣͰࣝผॲཧ͕ग़དྷΔ w αϯϓϧϓϩδΣΫτͷ͝հ w ΦϦδφϧͷֶशϞσϧͰΞϓϦΛ࡞Ζ͏ʂ 19
͝ਗ਼ௌ͋Γ͕ͱ͏͍͟͝·ͨ͠ 20