Developing and Deploying Deep Learning Based Computer Vision Systems - Alka Nair - MathWorks
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The document discusses the development and deployment of deep learning-based computer vision systems, focusing on applications like lane detection using platforms such as MATLAB and various neural network architectures like AlexNet. It outlines challenges in handling large datasets, the importance of transfer learning, and the acceleration of training and deployment through GPU coding. Additionally, it emphasizes the capacity for real-time applications and the seamless integration of deep learning with classical computer vision methods.
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- 1. 1Ā© 2017 The MathWorks, Inc. Developing and Deploying Deep Learning Based Computer Vision Systems Alka Nair Application Engineer
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- 3. 3 Deep Learning Applications in Computer Vision HIGHWAY_SCENE Classification Semantic SegmentationRain Detection and Removal Human Aware Navigation for Robots
- 4. 4 Lane Detection on a Tesla K40 GPU
- 5. 5 End-to-End Application: Lane Detection Transfer Learning AlexNet ā 1000 class classification Lane detection CNN Post-processing (find left/right lane points) Image Parabolic lane coefficients in world coordinates Left lane co-efficients Right lane co-efficients Output of CNN is lane parabola coefficients according to: y = ax^2 + bx + c MATLAB : A SINGLE PLATFORM FOR DEEP LEARNING TRAINING & DEPLOYMENT
- 6. 6 Deep Learning Challenges Big Data āŖ Handling large amounts of data āŖ Labeling thousands of images & videos Training and Testing Deep Neural Networks āŖ Accessing reference models from research āŖ Understanding network behavior āŖ Tuning hyperparameters and refining architectures āŖ Training takes hours-days Seamless Deployment onto embedded hardware Real world systems use more than deep learning Deep learning frameworks do not include āclassicalā computer vision Not a deep learning expert
- 7. 7 Access Large Sets of Images Handle Large Sets of Images Easily manage large sets of images - Single line of code to access images - Operates on disk, database, big-data file system imageData = imageDataStore(āvehiclesā) Easily manage large sets of images - Single line of code to access images - Operates on disk, database, big-data file system Organize Images in Folders (~ 10,000 images , 5 folders)
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- 12. 12 Deep Learning Challenges Big Data āŖ Handling large amounts of data āŖ Labeling thousands of images & videos Training and Testing Deep Neural Networks āŖ Accessing reference models from research āŖ Understanding network behavior āŖ Tuning hyperparameters and refining architectures āŖ Training takes hours-days Seamless Deployment onto embedded hardware Real world systems use more than deep learning Deep learning frameworks do not include āclassicalā computer vision Not a deep learning expert
- 13. 13 Transfer Learning Workflow Early layers Last layers 1 million images 1000s classes Load pretrained network Fewer classes Learn faster New layers Replace final layers 100s images 10s classes Training options Train network Trained Network Predict and assess network accuracy
- 14. 14 Import Pre-Trained Models and Network Architectures Pretrained Models āŖ AlexNet āŖ VGG-16 āŖ VGG-19 āŖ GoogLeNet āŖ Resnet50 āŖ InceptionV3 āŖ ResNet - 101 Import Models from Frameworks āŖ Caffe Model Importer (including Caffe Model Zoo) ā importCaffeLayers ā importCaffeNetwork āŖ TensorFlow-Keras Model Importer ā importKerasLayers ā importKerasNetwork Download from within MATLAB net = alexnet; net = vgg16; net = vgg19; net = googlenet; net = resnet50; net = inceptionv3; net = resnet101;
- 15. 15 Visualizations for Understanding Network Behavior āŖ Custom visualizations ā Example: Class Activation Maps Filters ā¦ Activations Deep Dream http://cnnlocalization.csail.mit.edu/Zhou_Learning_Deep_Features_CVPR_2016_paper.pdf
- 16. 16 Augment Training Images imageAugmenter = imageDataAugmenter('RandRotation',[-180 180]) Rotation Reflection Scaling Shearing Translation Colour pre-processing Resize / Random crop / Centre crop
- 17. 17 Transfer Learning Workflow Early layers Last layers 1 million images 1000s classes Load pretrained network Fewer classes Learn faster New layers Replace final layers 100s images 10s classes Training options Train network Trained Network Predict and assess network accuracy
- 18. 18 Transfer Learning Workflow Early layers Last layers 1 million images 1000s classes Load pretrained network Fewer classes Learn faster New layers Replace final layers 100s images 10s classes Training options Train network Trained Network Predict and assess network accuracy
- 19. 19 Transfer Learning Workflow Early layers Last layers 1 million images 1000s classes Load pretrained network Fewer classes Learn faster New layers Replace final layers 100s images 10s classes Training options Train network Trained Network Predict and assess network accuracy
- 20. 20 Training Deep Neural Networks trainingOptions āŖ Plot training metrics ā Training accuracy, smoothed training accuracy, validation accuracy ā Training loss, smoothed training loss, and validation loss āŖ Debug training ā Stop and check current state ā Save / load checkpoint networks ā Custom output function (stopping condition, visualization, etc.) āŖ Bayesian optimization for hyperparameter tuning Learn More
- 21. 21 Transfer Learning Workflow Early layers Last layers 1 million images 1000s classes Load pretrained network Fewer classes Learn faster New layers Replace final layers 100s images 10s classes Training options Train network Trained Network Predict and assess network accuracy MATLAB Provides Evaluation Frameworks for Different Classes of Deep Learning Problems
- 23. 23 Deep learning on CPU, GPU, multi-GPU and clusters Single CPU Single CPU Single GPU Single CPU Multiple GPUs On-prem server with GPUs Cloud GPUs (AWS, Azure, etc.) Deep Learning on Cloud Whitepaper
- 24. 24 Training in MATLAB is fast MATLAB is more than 4x faster than TensorFlow AlexNet CNN architecture trained on the ImageNet dataset, using batch size of 32, on a Windows 10 desktop with single NVIDIA GPU (Titan Xp). TensorFlow version 1.2.0.
- 25. 25 Deep Learning Challenges Big Data āŖ Handling large amounts of data āŖ Labeling thousands of images & videos Training and Testing Deep Neural Networks āŖ Accessing reference models from research āŖ Understanding network behavior āŖ Tuning hyperparameters and refining architectures āŖ Training takes hours-days Seamless Deployment onto embedded hardware Real world systems use more than deep learning Deep learning frameworks do not include āclassicalā computer vision Not a deep learning expert
- 26. 26 Algorithm Design to Embedded Deployment Workflow Conventional Approach Desktop GPU High-level language Deep learning framework Large, complex software stack 1 Desktop GPU C++ C/C++ Low-level APIs Application-specific libraries 2 C++ Embedded GPU C/C++ Target-optimized libraries Optimize for memory & speed 3 Challenges ā¢ Integrating multiple libraries and packages ā¢ Verifying and maintaining multiple implementations ā¢ Algorithm & vendor lock-in
- 27. 27 GPU Coder for Deployment: New Product in R2017b Neural Networks Deep Learning, machine learning Image Processing and Computer Vision Image filtering, feature detection/extraction Signal Processing and Communications FFT, filtering, cross correlation, 7x faster than state-of-art 700x faster than CPUs for feature extraction 20x faster than CPUs for FFTs GPU Coder Accelerated implementation of parallel algorithms on GPUs
- 28. 28 Algorithm Design to Embedded Deployment Workflow with GPU Coder MATLAB algorithm (functional reference) Functional test1 Deployment unit-test 2 Desktop GPU C++ Deployment integration-test 3 Desktop GPU C++ Real-time test4 Embedded GPU .mex .lib Cross-compiled .lib Build type Call CUDA from MATLAB directly Call CUDA from (C++) hand- coded main() Call CUDA from (C++) hand-coded main().
- 29. 29 CUDA Code Generation from GPU Coder
- 30. 30
- 31. 31 End-to-End Application: Lane Detection Transfer Learning AlexNet ā 1000 class classification Lane detection CNN Post-processing (find left/right lane points) Image Parabolic lane coefficients in world coordinates Left lane co-efficients Right lane co-efficients Output of CNN is lane parabola coefficients according to: y = ax^2 + bx + c MATLAB : A SINGLE PLATFORM FOR DEEP LEARNING TRAINING & DEPLOYMENT https://tinyurl.com/ybaxnxjg
- 32. 32 Alexnet Inference on Intel CPUs MATLAB (R2017b Release 2) TensorFlow MXNet Caffe2
- 33. 33 Alexnet Inference on NVIDIA Titan Xp MATLAB GPU Coder (R2017b) TensorFlow (1.2.0) Caffe2 (0.8.1) Framespersecond Batch Size CPU Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz GPU Pascal Titan Xp cuDNN v5 Testing platform MXNet (0.10) MATLAB (R2017b) 2x 7x5x
- 34. 34 Alexnet inference on NVIDIA GPUs 0 1 2 3 4 5 6 7 8 9 CPU resident memory GPU peak memory (nvidia-smi) Memoryusage(GB) Batch Size1 16 32 64 CPU Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50 GHz GPU Tesla K40c Py-Caffe GPUCoder TensorFlow MATLABw/PCT C++-Caffe
- 35. 35 Design Deep Neural Networks in MATLAB and Deploy with GPU Coder Design Deep Learning & Vision Algorithms Highlights āŖ Manage large image sets āŖ Easy access to models like AlexNet, GoogleNet āŖ Pre-built training frameworks āŖ Automate ground truth labeling apps Highlights āŖ Automate optimized CUDA code generation with GPU Coder āŖ Deployed models upto 4.5x faster than Caffe2 and 7x faster than Tensor High Performance Deployment
- 36. 36 Deep Learning Onramp Free Introductory Course Available Here
- 37. 37 Visit MathWorks Booth to Learn More HIGHWAY_SCENE Classification Car Car CarDetection Lane Lane Regression Semantic Segmentation