Caffe2C: A Framework for Easy Implementation of CNN-based Mobile Applications
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1 Caffe2C: A Framework for Easy Implementation of CNN-based Mobile Applications Ryosuke Tanno and Keiji Yanai Department of Informatics, The University of Electro-Communications, Tokyo
2 1. INTRODUCTION
3 Deep Learning(DNN,DCNN,CNN) Deep Learning achieved remarkable progress E.g. Audio Recognition, Natural Language Processing, Especially, in Image Recognition, Deep Learning gave the best performance Outperform even humans such as recognition of 1000 object(he+, Delving deep into rectifier, 2015) % 75% SIFT+BOF 85% 88.3% 93.3% 96.4% 94.9% Deep Learning Deeeeeeeep Trained Human Outperform Human!
4 Deep Learning Framework Many Deep Learning Framework have emerged E.g. Caffe, TensorFlow, Chainer
5 What is Caffe? Convolution Architecture For Feature Extraction(CAFFE) Open Framework, models and examples for Deep Learning Focus on Compuer Vision Pure C++/CUDA architecture for deep learning Command line, Python MATLAB interface Fastest processing speed Caffe is the most popular framework in the world
6 Bring to CNN to Mobile There are many attempts to archive CNN on the mobile Require a high computational power and memory High Computational Power and Memory are Bottleneck!!
7 How to train a model by caffe? 3 files are required for Training -> Output: Model 3 files: Network definition, Mean, Label Dataset Files Training Output Caffemodel Use these 4 files on mobile Network Mean Label 3 files
8 Use the 4 Files by Caffe on the Mobile We currently need to use OpenCV DNN module not optimized for the mobile devices their execution speed is relatively slow Network Mean Label Model 4 files
9 We create a Caffe2C which converts the CNN model definition files and the parameter files trained by Caffe to a single C language code that can run on mobile devices Network Mean Label Model Objective 4 files Caffe2C Single C code Caffe2C makes it easy to use deep learning on the C language operating environment Caffe2C achieves faster runtime in comparison to the existing OpenCV DNN module
10 Objective In order to demonstrate the utilization of the Caffe2C, we have implemented 4 kinds of mobile CNN-based image recognition apps on ios.
11 Contributions 1. We create a Caffe2C which converts the model definition files and the parameter files of Caffe into a single C code that can run on mobile devices 2. We explain the flow of construction of recognition app using Caffe2C 3. We have implemented 4 kinds of mobile CNN-based image recognition apps on ios.
12 2. CONSTRUCTION OF CNN-BASED MOBILE RECOGNITION SYSTEM
13 Caffe2C In order to use the learned parameters by Caffe on mobile devices, it is necessary to currently use the OpenCV DNN module not optimized, relatively slow We create a Caffe2C which converts the CNN model definition files and the parameter files trained by Caffe to a single C language code We can use parameter files trained by Caffe on mobile devices
14 Caffe2C Caffe2C achieves faster execution speed in comparison to the existing OpenCV DNN module Runtime[ms] Caffe2C vs. OpenCV DNN(Input size: 227x227) Caffe2C OpenCV DNN AlexNet iphone 7 Plus ipad Pro iphone SE Speedup Rate: About 15X~
15 Reasons for Fast Execution 1. Caffe2C directly converts the Deep Neural Network to a C source code Network Mean Label Model Caffe2C Single C code Caffe2C OpenCV DNN Execution like Compiler Execution like Interpreter
16 Reasons for Fast Execution 2. Caffe2C performs the pre-processing of the CNN as much as possible to reduce the amount of online computation Compute batch normalization in advance for conv weight. Network Mean Label Model 4 files Caffe2C Single C code 3. Caffe2C effectively uses NEON/BLAS by multi-threading
17 Deployment Procedure 1. Train Deep CNN model by Caffe 2. Prepare model files 3. Generate a C source code by Caffe2C automatically 4. Implement C code on mobile with GUI code Train Phase Deep CNN Trained Deep CNN Model Model Preparation Caffemodel Network Mean Label Convert C code 3 Caffe2C Implement on Mobile
18 3. IMAGE RECOGNITION SYSTEM FOR EVALUATION
19 Image Recognition System for evaluation In order to demonstrate the utilization of the Caffe2C, we have implemented four kinds of mobile CNNbased image recognition apps on ios We explain image recognition engine used in the ios application
20 CNN Architecture A representative architectures are AlexNet VGG-16 GoogleNet or NIN AlexNet VGG-16 Network-In-Network
21 CNN Architecture The number of weights in AlexNet and VGG-16 is too much for mobile. GoogLeNet is too complicated for efficient parallel implemen -tation. (It has many branches.)
22 CNN Architecture We adopt Network-in-Network (NIN). No fully-connected layers (which bring less parameters) Straight flow and consisting of many conv layers relatively smaller than the other architectures It s easy for parallel implementation. Efficient computation for conv layers is needed! Network-In-Network(NIN)
23 patch 1 patch 2 patch 3 patch 4 patch 5 patch 1 patch 2 patch 3 patch 4 patch 5 patch 1 patch 2 patch 3 patch 4 patch 5 patch 1 patch 2 patch 3 patch 4 patch 5 patch 1 patch 2 patch 3 patch 4 patch 5 Fast computation of conv layers - efficient GEMM with 4 cores and BLAS/NEON - Conv = im2col + GEMM (Generic Matrix Multiplication) conv. kernels kernel 1 kernel 2 kernel 3 kernel 4 im2col patch 1 patch 5 patch 2 patch 3 patch 4 input feature maps matrix multiplication (=conv. layer computation) Parallel computation over multiple cores Inside each core NEON or BLAS is used. kernel 1 kernel 2 kernel 3 kernel 4 Core1 Core2 Core3 Core4
24 Fast Implementation on Mobile Speeding up Conv layers Speeding up GEMM computation of conv layer is decomposed into im2col operation and generic matric multiplications(gemm) Multi-threading: Use 2cores in ios, 4 cores in Android in parallel SIMD instruction(neon in ARM-based processor) Total: ios: 2Core*4 = 8calculation, Android: 4Core*4 = 16 calculation BLAS library(highly optimized for ios not optimized for Android) BLAS(iOS: BLAS in ios Accelerate Framework, Android: OpenBLAS)
25 Evaluation: Processing time ios: BLAS >> NEON, Android: BLAS << NEON For ios, using BLAS in the ios Accelerate Framework is the best choice. For Android, using NEON (SIMD instruction) is better than OpenBLAS. Recognition Time[ms] BLAS vs. NEON NEON BLAS Devices BLAS ios iphone 7 Plus Accelerate ios ipad Pro Accelerate ios iphone SE Accelerate Android GALAXY Note 3 OpenBLAS Highly optimized
26 Comparison to FV-based Previous Method Deep Learning with UEC-FOOD100 dataset Much improved ( 65.3% 81.5% (top-1) ) Even for 160x160 improved ( 65.3% 71.5% ) Top-N Classification 100.0% Accuracy 95.0% 90.0% Top1: 81.5% Top5: 96.2% 85.0% 80.0% 75.0% 70.0% 65.0% 60.0% Top1: 65.3% Top5: 86.7% AlexNet NIN 5layer [104ms] NIN 4layer [67ms] NIN 4layer (160x160) [33ms] FV (Color+HOG) [65ms] Top-N Kept almost the same
27 4. MOBILE APPLICATIONS
28 4 ios Applications We have implemented 4 kinds of mobile CNN-based image recognition apps on ios Food recognition app: DeepFoodCam Bird recognition app: DeepBirdCam Dog recognition app: DeepDogCam Flower recognition app: DeepFlowerCam
29 DeepFoodCam Recognize 101 classes including 100 food classes and one nonfood class Training Phase fine-tuned the CNN with 101 class images totally 20,000 images UECFOOD-100 and non-food collected from Twitter Accuracy Target Top-1 Top-5 Food 101 class 74.5% 93.5%
30 Recognize 200 bird class DeepBirdCam Training Phase fine-tuning CNN with 6033 images of Caltech-UCSD Birds 200 Dataset Accuracy Target Top-1 Top-5 Bird 200 class 55.8% 80.2%
31 Recognize 100 dog class DeepDogCam Training Phase fine-tuning CNN with 150 and over images per class of Stanford Dogs Dataset Dataset Accuracy Target Top-1 Top-5 Dog 100 class 69.0% 91.6%
32 DeepFlowerCam Recognize 102 flower class Training Phase fine-tuning CNN with 80 and over images per class of 102 Category Flower Dataset Accuracy Target Top-1 Top-5 Flower 102 class 64.1% 85.8%
33 4 ios Applications We have implemented 4 kinds of mobile CNN-based image recognition apps on ios Food recognition app: DeepFoodCam Bird recognition app: DeepBirdCam Dog recognition app: DeepDogCam Flower recognition app: DeepFlowerCam If you prepare training data, you can create mobile recognition apps in a day!!
34 Conclusions 1. We create a Caffe2C which converts the model definition files and the parameter files of Caffe into a single C code that can run on mobile devices 2. We explain the flow of construction of recognition app using Caffe2C 3. We have implemented 4 kinds of mobile CNN-based image recognition apps on ios.
35 Additional work We implemented apply our mobile framework into real-time CNN-based mobile image processing such as Neural Style Transfer
36 ios App is Available! DeepFoodCam Object Recognition Thank you for listening Neural Style Transfer ios App is Available! RealTimeMultiStyleTransfer
37 Extension of NIN adding BN, 5layers, multiple image size Modified models (BN, 5layer, multi-scale) adding BN layers just after all the conv/cccp layers replaced 5x5 conv with two 3x3 conv layers reduced the number of kernels in conv 4 from 1024 to 768 replaced fixed average pooling with Global Average Pooling 4layers Multiple image size 5layers+BN 227x x x160 Trade-off: Accuracy vs speed Global Average Pooling (GAP) 227x ms 78.8% 180x ms 76.0% 160x ms 71.5%
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