Have you ever felt tired of writing every ML/AI application from scratch and wished there would be a tool to save you time? Try oneML! All software developers have, at least once, experienced the challenge of effectively leveraging and deploying Machine Learning due to complex infrastructure and a high variety of models and frameworks to select. It also requires high computational power, heterogeneous production environments, and various programming languages. oneML was developed specifically to tackle that particular problem. As a fully-fledged C++ SDK with an API for a variety of applications, It can be deployed on any hardware and operating system. As It is made of several task-specific macro modules, oneML achieves the most efficient SDK performance and is easy to extend by simply adding a new module. Unfold the power of high performance and usability with oneML, another promising product that will make developers’ life easier. No need to waste time and resources by reinventing the wheel, instead, create something awesome on top of it! Let's try it yourself with this introduction and sample applications to showcase oneML functionalities and possible use cases: face detection, face identification, face embedding, and vehicle detection. Simply download oneML and follow the instructions from one of the following links: Explore and download a demo version: https://github.com/sertiscorp/on... Read more: https://sertiscorp.medium.com/on... What is oneML"? is a fully-fledged C++ software development kit (SDK) providing APIs for several different AI/ML applications aiming to achieve the most efficient performance and making the SDK easy to extend by simply adding a new module. Potentially, it can be deployed on any target including CPU, GPU, and CUDA, and also any platform such as Android, iOS, embedded Linux, Linux, and Windows. Moreover, the library provides API bindings in other programming languages such as Java, Python, C#, and Golang. Also, is automatically built and tested every time something changes in the codebase. oneML oneML oneML oneML supported components On the customer side, there is no prerequisite apart from having a compatible deployment environment with at least one combination of supported programming languages, operating systems, and target hardware and architectures. If the environment has high computational power, the SDK will run faster but it won’t affect the accuracy. Getting started This section describes steps for quickly testing library. We will download library and models. Then, we build and run sample C++ applications on architecture. There are 2 separate guidelines for Linux and Windows users. oneML oneML x86_64 For other languages and architectures, please refer to the section. NOTE: Setup and Build For Linux users Step 1: Build a docker image We recommend setting up an environment for with a docker container. A docker image can be built from in the folder. Here, we are going to build a base image and another image for CPU runtime. Run the below command to build the image. oneML Dockerfile docker $ docker build -t oneml-bootcamp:cpu-base -f docker/Dockerfile — build-arg base_image=ubuntu:20.04 .
$ docker build -t oneml-bootcamp:cpu -f docker/Dockerfile.cpu — build-arg base_image=oneml-bootcamp:cpu-base . Step 2: build sample applications We will run a docker container with the image that we built in the previous step, and mount this repository folder to the container at path. We execute inside the container to run the next following steps. oneml-Bootcamp: CPU /workspace bash $ docker run -it — name oneml-bootcamp -v $PWD:/workspace oneml-bootcamp:cpu /bin/bash -c “cd /workspace && /bin/bash” Then, inside the container, we download a library and models and build sample C++ applications with the script. oneML build.sh $ ./build.sh -t x86_64 -cc — clean The library and models are downloaded to . oneML assets/binaries/x86_64 $ tree assets/binaries/x86_64

├── bindings
├── config.yaml
├── include
├── lib
├── oneml-bootcamp-x86_64.tar.gz
├── README.md
└── share The compiled C++ applications are in the folder. bin $ tree bin

├── face_detector
├── face_embedder
├── face_id
├── face_verification
└── vehicle_detector Step 3: run sample applications We will run face detection and vehicle detection applications. Our sample applications run models with images in the folder. Change the directory to the folder. oneML assets/images bin $ cd bin To run the face detector application, simply run this command. $ ./face_detector

Faces: 1
Face 0
Score: 0.997635
Pose: Front
BBox: [(43.740112, 85.169624), (170.153168, 173.094971)]
Landmark 0: (109.936066, 95.690453)
Landmark 1: (149.844360, 94.360649)
Landmark 2: (130.055618, 117.397400)
Landmark 3: (112.128708, 137.498520)
Landmark 4: (146.398651, 136.513870) To run the vehicle detection application, simply run this command. $ ./vehicle_detector

Vehicles: 8
Vehicle 0
Score: 0.907127
BBox[top=302.584076,left=404.385437,bottom=486.317841,right=599.864502]
Vehicle 1
Score: 0.899688
BBox[top=301.987000,left=651.770996,bottom=432.352997,right=920.845947]
Vehicle 2
Score: 0.875428
BBox[top=314.573608,left=143.590546,bottom=445.937286,right=367.556488]
Vehicle 3
Score: 0.873904
BBox[top=237.489685,left=100.689484,bottom=303.421906,right=279.191864]
Vehicle 4
Score: 0.842179
BBox[top=243.125473,left=328.062103,bottom=307.927338,right=473.017853]
Vehicle 5
Score: 0.822146
BBox[top=238.680069,left=563.760925,bottom=308.430756,right=705.746033]
Vehicle 6
Score: 0.653955
BBox[top=215.012100,left=477.994904,bottom=252.589249,right=547.705505]
Vehicle 7
Score: 0.620528
BBox[top=213.333237,left=641.528503,bottom=249.301697,right=742.615601] For Windows users Step 0: Install the required tools To build C++ applications on Windows, we need to install these tools as follows: oneML Microsoft Visual Studio 16 2019 or newer CMake 3.17 or newer Step 1: build sample applications We will download library and models, and build sample C++ applications with the script. oneML build.bat > build.bat -t msvc-x64 -cc — clean The library and models are downloaded to . oneML assets\binaries\msvc-x64 > dir assets\binaries\msvc-x64

├── bindings
├── config.yaml
├── include
├── lib
├── oneml-bootcamp-msvc-x64.tar.gz
├── README.md
└── share The compiled C++ applications are in the folder. bin\Release > dir bin\Release

├── face_detector.exe
├── face_embedder.exe
├── face_id.exe
├── face_verification.exe
└── vehicle_detector.exe Step 2: run sample applications We will run face detection and vehicle detection applications. Our sample applications run models with images in the folder. Change the directory to folder. oneML assets\images bin\Release > cd bin\Release To run the face detection application, simply run this command. > face_detector.exe

Faces: 1
Face 0
Score: 0.997635
Pose: Front
BBox: [(43.740120, 85.169617), (170.153168, 173.094971)]
Landmark 0: (109.936081, 95.690453)
Landmark 1: (149.844376, 94.360649)
Landmark 2: (130.055634, 117.397392)
Landmark 3: (112.128716, 137.498520)
Landmark 4: (146.398636, 136.513870) To run the vehicle detection application, simply run this command. > vehicle_detector.exe

Vehicles: 8
Vehicle 0
Score: 0.907128
BBox[top=302.583893,left=404.385590,bottom=486.317871,right=599.864380]
Vehicle 1
Score: 0.899688
BBox[top=301.986755,left=651.771118,bottom=432.352966,right=920.846069]
Vehicle 2
Score: 0.875427
BBox[top=314.573425,left=143.590775,bottom=445.937408,right=367.556946]
Vehicle 3
Score: 0.873901
BBox[top=237.489792,left=100.689354,bottom=303.421936,right=279.191772]
Vehicle 4
Score: 0.842178
BBox[top=243.125641,left=328.062012,bottom=307.927002,right=473.017883]
Vehicle 5
Score: 0.822146
BBox[top=238.680267,left=563.760864,bottom=308.430756,right=705.746338]
Vehicle 6
Score: 0.653957
BBox[top=215.012009,left=477.994873,bottom=252.589355,right=547.705811]
Vehicle 7
Score: 0.620529
BBox[top=213.333160,left=641.528442,bottom=249.301712,right=742.615662] Setup and build Depending on your OS and which programming language you would like to use, the setup and build process are going to be slightly different. Please refer to the relevant section below. Linux On Linux machines, it is always advised to use to create a reproducible workspace and not have to worry about dependencies. If you don’t want to use , feel free to set up your local environment to match the one provided in our . docker docker Dockerfiles Docker build There are multiple Dockerfiles available based on the HW to be targeted for the deployment as well as if the device requires specific build tools or dependencies. There are two different types of docker images: docker images are supposed to provide all the common tools and libraries shared by all the others, more specific, images built on top of them base device/OS-specific, e.g. CPU, GPU, android For a normal CPU environment: $ docker build -t oneml-bootcamp:cpu-base -f docker/Dockerfile — build-arg base_image=ubuntu:20.04 .
$ docker build -t oneml-bootcamp:cpu -f docker/Dockerfile.cpu — build-arg base_image=oneml-bootcamp:cpu-base . For a GPU environment: $ docker build -t oneml-bootcamp:gpu-base -f docker/Dockerfile — build-arg base_image=nvidia/cuda:11.5.1-cudnn8-runtime-ubuntu20.04 .
$ docker build -t oneml-bootcamp:gpu -f docker/Dockerfile.gpu — build-arg base_image=oneml-bootcamp:gpu-base . For an Android environment: $ docker build -t oneml-bootcamp:cpu-base -f docker/Dockerfile — build-arg base_image=ubuntu:20.04 .
$ docker build -t oneml-bootcamp:android -f docker/Dockerfile.android — build-arg base_image=oneml-bootcamp:cpu-base . Apps build We provide a script that will set up the project, prepare the environment and build all the artifacts necessary to run the sample applications. bash $ ./build.sh — help will describe the build script. Here are some examples: To build apps for target: C++ x86_64 $ ./build.sh -t x86_64 -cc — clean To build apps for target: Python aarch64-Linux-gnu $ ./build.sh -t aarch64-linux-gnu -py — clean To build apps for target: C# x86_64 $ ./build.sh -t x86_64 -cs — clean To build apps for target: Java x86_64 $ ./build.sh -t x86_64 -jni — clean To build apps for target: Android aarch64-Linux-android $ ./build.sh -t aarch64-linux-android -jni — clean On , Cuda-enabled GPU is also supported. To build apps for target: x86_64 C++ x86–64-Cuda $ ./build.sh -t x86_64-cuda -cc — clean If is not specified, the existing artifacts will be used and the old build files will not be deleted. --clean oneML Windows We provide support for our library and sample applications for Windows OS as well. Unfortunately, there is no easy way to use our Dockerfiles in this environment, thus the user has to rely on their local environment and make sure all the dependencies are installed before proceeding with the build process and running the applications. Requirements Only Windows 10/11 64-bit is currently supported. Dependencies The following dependencies must be installed for the project to build and run successfully: Microsoft Visual Studio 16 2019 or newer to pull some data from the internet curl to unpack some archives tar CMake 3.17 or newer (for build only) C++ Python 3.6 or newer and (for build only) pip Python (for build only) .NET6 C# JDK 1.8 (for build only) Java Android Studio (for build only) Android Golang 1.15 (for build only) Golang Powershell, a recent version (for any build, but ) C++ Apps build We provide a script that will set up the project, prepare the environment and build all the artifacts necessary to run the sample applications. batch > build.bat — help will describe the build script. Here are some examples: To build apps for target: C++ music-x64 > build.bat -t msvc-x64 -cc — clean To build apps for target: Python msvc-x64 > build.bat -t msvc-x64 -py — clean To build apps for target: C# msvc-x64 > build.bat -t msvc-x64 -cs — clean To build apps for target: Java msvc-x64 > build.bat -t msvc-x64 -jni — clean If is not specified, the existing artifacts will be used and the old build files will not be deleted. --clean oneML CUDA support Currently, the target supports only Cuda 11.x runtime on Linux only. We also plan to support Cuda 10.2 in the future. Moreover, it is built with backward compatibility in mind. supports the following Cuda compute capabilities x86–64-cuda x86–64-cuda 7.5 7.0 6.1 6.0 5.2 5.0 For compute capabilities later than , it might not work. 7.5 API reference C++/Java/Python/C# APIs Golang APIs Usage It is possible to find more details about the usage in each programming language-specific folder in the folder. You can click on the following links: apps C++ apps Python apps C# apps Java apps Golang apps For all the applications, it is possible to set the LOG level of by settings the environment variable to one of the following values: oneML ONEML_CPP_MIN_LOG_LEVEL , to log all the information INFO , to log only warnings and more critical information WARNING , to log only errors and fatal crashes ERROR , to only log fatal crashes FATAL Use cases A wide variety of face-related and ALPR-related applications are available: : detecting numbers of faces in an image by identifying the confidence score, bounding box, landmarks, and poses of each face Face or face masks detection : transforming an image into an embedding which is a list of numbers Face embedding : registering a new identity into a database, validating images or embeddings, and predicting an identity based on identities in the database by calculating the nearest identity distance, combined identity distance, and true/false identifiability of the image Face ID : classifying a spoof probability of the image PAD (Person Attack Detection) : performing liveness detection on images or video by detecting blinks Challenge response : validating a person's identity based on an identification document and a camera picture EKYC (Electronic Know Your Customer) : detecting vehicles and numbers of vehicles using confidence score and bounding box, classifying vehicles, detecting make or model, as well as detecting license plates Vehicle analysis application : Provide an additional set of utilities such as reading an image from a file and cropping and aligning a face in an image Utils Notes A licensing system is integrated into oneML providing features and usage control such as license keys, online or offline activation, expiration date, and trial. For those who are interested to try it out for free for a limited time, let’s explore the public repository via this link below. https://github.com/sertiscorp/oneML-bootcamp

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Write Machine Learning Apps Faster With the OneML SDK

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