Deep Learning - system view
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What are the models, development process, development tools and common platforms for deep learning development
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Deep Learning - system view
- 1. 1 Deep Learning From black magic to structured programming
- 2. 2 Agenda ▪Introduction ▪Tools / Model Types ▪Classifiers to instance segmentors ▪Process ▪Program? Train? ▪Data ▪Where to get it? ▪Platforms ▪Optimization ▪Video Analytics
- 3. 3 Introduction What is Deep Learning?
- 4. 4 What is Deep Learning? ▪Deep Learning is not “Black Magic” ▪Deep Learning is using common statistics tools ▪Deep Learning is using iterative convergence process to find a local minimum point which minimize the loss (“best local solution”)
- 5. 5 What is Deep Learning ? Gradient Descent ! Neural Networks Convolutional Neural Networks Back Propagation!? X Impossible Math? X
- 6. 6 Deep Learning for system eng. & Architects ▪Set of Tools ▪Simple library ▪Development process ▪Non-standard programming
- 8. 8 The tools ▪Most common tools: ▪Classifier ▪Detector (Box Detectors) ▪Segmentor ▪Instance Segmentor ▪Time series networks
- 9. 9 Classifiers - 2 ▪Answer on the existence of X (in image?) ▪Number classifier 0-9 (first CNN) ▪Common objects (Car, Chair, Cat…) ▪Cancer / No Cancer ▪Gun shot / broken glass ▪Most classifiers use some form of CNNs
- 10. 10 Classifiers ▪Single classifier could classify 100 objects ▪Most classifiers use COCO (~80 classes) as default ▪Example of La-net ▪First CNN for digits ▪Over 20 years old!!!
- 11. 11 BOX Detectors ▪Box detectors detects the location of the object ▪Used to detect: ▪People, face, corona mask ▪car, road signs ▪cats
- 12. 12 BOX Detectors ▪Box detectors could detect more than one class ▪Box detectors could detect overlapping objects ▪Known box detectors: ▪YOLO3 (most used) YOLO5 (Latest common model) ▪Yolo2PP, YOLOR (Fast) ▪YOLACT-EDGE (fast and accurate) ▪SSD / SSD2 / Mobile-SSD
- 13. 13 Segmentor and Multi-Instance Segmentor ▪Segmentor ▪defines class for each pixel ▪Pixel(X,Y) belongs to cat ▪FCN-8 basic implementation ▪Does not separate overlapping objects!!! ▪Common Segmentor U-Net
- 14. 14 Multi-Instance Segmentor ▪Multi-instance segmentor ▪Separate pixel to classes ▪Separate a class to instances ▪Pixel(X,Y) belongs to cat3
- 15. 15 Multi-Instance Segmentor - Implementation ▪Common / Simple implementation – MRCNN ▪Detectoron2 - Good implementation by Facebook
- 16. 16 (Time) series network ▪Runs on sequential data ▪Number of visitors in site ▪ECG data ▪Solar spot data ▪Single variable Data ▪Multi Variable ▪Example: LSTM, RNN
- 17. 17 Landmarks extraction ▪Landmark extraction is done for: ▪Face recognition ▪Skeleton Pose estimation ▪Hand Gestures
- 20. 20 Data ▪Data gathering (Initial) ▪Cameras / datasets ▪Data generation ▪Graphic engine (Unity / Omnviverse) ▪GAN ▪Data exploration ▪Anomaly detection ▪Class bias ▪Errored data ▪Data cleaning ▪Setting data pipes
- 21. 21 Training ▪Model selection Consideration ▪Performance constraints (run under < 1 TFLOPS) ▪Accuracy requirements ▪Model ▪Our own network ▪Existing network with/out weights ▪Training ▪No weights - from scratch ▪ Weights – Transfer learning ▪Hyper-param optimization ▪learning rate, decay, batch size, optimizer
- 22. 22 KPI – Measuring the results ▪Classifier accuracy ▪Percentage detected out of total objects ▪False detection ▪Detector accuracy ▪Classifier accuracy + ▪Box Accuracy ▪Segmentor Accuracy ▪Class accuracy ▪Segmentation accuracy – IoU (Intersection of Union) ▪Instance segmentor accuracy…..
- 23. 23 Model Optimization ▪For which platform? CPU, GPU, TPU? ▪What type of GPU/TPU? ▪How many cores? ▪What type of cores (FP32, FP16, Int 16, Int8) ▪What batch to use?
- 24. 24 Deployment and Automation ▪How to install the environment? ▪Python ▪Libraries ▪SDKs? ▪In Docker ? ▪How to automate the training and deployment platforms?
- 26. 26 Data sources ▪Open-source data sets ▪Kaggle ▪Github ▪Buy tagged data ▪Tagging ourselves ▪Tagging service provider
- 27. 27 Dataset Challenges ▪Existence – does the data exists somewhere ▪Faces, numbers, clothing, animals, cars, fonts ▪Can we use the data to train freely? ▪Datasets Has license like code ▪Does it fit my needs? ▪Data is RGB we need IR ▪Data is from close range we need distant range ▪Good plants we need sick plants ▪Cancer non-cancer image ration is 1:4.3*10^5 ▪Data fine but too little
- 28. 28 Datasets ▪Know used datasets ▪MNIST – numbers ▪MNIST fashion – clothing ▪COCO – most used Dataset ▪Kaggle ▪Many dataset exists in Kaggle ▪Some closed for competition many open ▪Buy dataset ▪Create your own ▪Take images (10000? automate?) ▪Tag on your own / External service
- 30. 30 Deployment and Automation ▪For code we have Git and Git lab for CI/CD ▪Can we use it for Deep Learning ▪Requirements ▪Manage data gathering ▪Store data versioning ▪Create and store model versioning ▪Automatic training + KPI Measurements ▪Automatic deployment
- 31. 31 Deep Learning Automation - 2 ▪MLOP Platforms ▪ClearML (Allegro) ▪Converge.IO (Intel) ▪Igazio ▪Neptune ▪SageMaker
- 32. 32 Optimization ▪CNN Models are require TOO much processing ▪Optimization process ▪Dev platform to single representation ▪Pruning ▪Convert type
- 33. 33 Accelerator software platforms ▪Model porting and optimization ▪Nvidia TensorRT ▪Intel OpenVino ▪XILINX tools ▪Video processing and analytics platforms ▪NVIDIA DeepStream ▪Intel OpenVino ▪XILINX ▪All video analytics SDKs are based on GStreamer ▪Robotics ▪NVIDIA ISAAC (ROS / GEMS integration)