https://github.com/leifermendez/youtube-live-face-app-angular
🤖 Empieza a realizar aplicaciones asombrosas haciendo uso de MachineLearning con TensorFlow.js por el lado de navegador. En este caso realizamos reconocimiento facial
https://github.com/leifermendez/youtube-live-face-app-angular
tensorflow
Last synced: 11 months ago
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🤖 Empieza a realizar aplicaciones asombrosas haciendo uso de MachineLearning con TensorFlow.js por el lado de navegador. En este caso realizamos reconocimiento facial
- Host: GitHub
- URL: https://github.com/leifermendez/youtube-live-face-app-angular
- Owner: leifermendez
- Created: 2021-02-04T11:34:06.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-08-18T02:56:50.000Z (almost 5 years ago)
- Last Synced: 2025-08-04T01:47:14.581Z (12 months ago)
- Topics: tensorflow
- Language: TypeScript
- Homepage:
- Size: 10.3 MB
- Stars: 19
- Watchers: 1
- Forks: 15
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# FaceApp
This project was generated with [Angular CLI](https://github.com/angular/angular-cli) version 11.1.1.
## Development server
Run `ng serve` for a dev server. Navigate to `http://localhost:4200/`. The app will automatically reload if you change any of the source files.
## Code scaffolding
Run `ng generate component component-name` to generate a new component. You can also use `ng generate directive|pipe|service|class|guard|interface|enum|module`.
## Build
Run `ng build` to build the project. The build artifacts will be stored in the `dist/` directory. Use the `--prod` flag for a production build.
## Running unit tests
Run `ng test` to execute the unit tests via [Karma](https://karma-runner.github.io).
## Running end-to-end tests
Run `ng e2e` to execute the end-to-end tests via [Protractor](http://www.protractortest.org/).
## Further help
To get more help on the Angular CLI use `ng help` or go check out the [Angular CLI Overview and Command Reference](https://angular.io/cli) page.