Detector is .net application that demonstrates how to use different type of dotnet components (e.g. ML.NET, Blazor, Shared Class Library and Web.API).
The high-level schematic view of the application, components and features is shown on the image below:
graph TD;
A(Detector) --> B(Machine Learning)
style A stroke:#255,stroke-width:2px;
style B fill:#99F,stroke:#111,stroke-width:2px;
A-->C(Blazor Client App);
style C fill:#f50,stroke:#111,stroke-width:2px;
A-->F(Shared Library);
style F fill:#f099,stroke:#111,stroke-width:2px;
F-->B
F-->C
F-->D
F-->FE(EF Model, DTO,... )
A-->D(Web API);
style D fill: #f96,stroke:#111,stroke-width:2px;
B-->E(ML.NET);
E-->EE(Tensorflow.NET);
EE-->EEE(Image Classifications);
EEE-->EEEE(Transfer Learning);
C-->CA(Web Assembly);
D-->DA(Minimal API Pattern);
DA-->DAC(DI,Mediator, Rep.., ....);
D-->DB(Functional tests)
DB-->DBB(Unit test)
DB-->DBC(Integration test)
The component shares the common code across all components:
- DTO data transformation objects
- Model - EF model
- Interfaces for different types in order to implement software patterns e.g. repository, mediator, etc.
Web API Detector is based on the latest design pattern for building Web API based services called Minimal API. The pattern simplifies the implementation of the several very important parts of every Webapplications:
- authentications and authorizations
- middleware, mapping and routing in Web APi,
- Mediator pattern and CQRS implementation in Web API,
- Unit and intergation tests
The machine larning part is based on console application that implements image classification using ML.NET and Tensforflow.NET. The application uses Inceptionv3 pretrained model from the official tensorflow site
The solution looks like very often scenario where you as a .NET Developer need to implement the ML solution into your existing or new .NET project. So the Blazor App is your standard .NET project (WinForms, WPF, WebApp ...) app that you asked to implement ML solution. The task for you to develop a machine learning model in order to detect Nokia 3310 legendary phone from loaded image.
Within the solution the following projects are implemented:
Nokia3310.Detector.App- Blazor App the application which consumes the ML model. -˛Nokia3310.ModelTrainer- Console App for building, training and preparing the images and model.Nokia3310.ML.Shared- .NET Standard library which shares the implementation needed for both projects.
The image below show the Workflow and the implementation of the solution:
Blazor App is simple application consists of two pages: - About - Detector (index) page
The Nokia3310.ModelTrainer project contains the code for image preparation and loading into the application memory. The project is clasic console app, capable to train, evaluate and save the model. Once the model is well trained, the zip file is saved on disk, and deployed in the Detector Bazor app.
The Trainer app implements the ML.NET Image Classification machine learning algorithm, that uses Tensorflow pretrained model Inception.V3 and uses Transfer Learning in order to train the Nokia3310 model detector.
