This is the Windows app named dlib whose latest release can be downloaded as v19.23.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named dlib with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is about 1 to 4. The library is tested regularly on MS Windows, Linux, and Mac OS X systems. No other packages are required to use the library, only APIs that are provided by an out of the box OS are needed. There is no installation or configure step needed before you can use the library. All operating system specific code is isolated inside the OS abstraction layers which are kept as small as possible.
- Conventional SMO based Support Vector Machines for classification and regression
- Reduced-rank methods for large-scale classification and regression
- A tool for solving the optimization problem associated with structural support vector machines
- Structural SVM tools for object detection in images as well as more powerful deep learning tools for object detection
- An online kernelized centroid estimator/novelty detector and offline support vector one-class classification
- Clustering algorithms, linear or kernel k-means, Chinese Whispers, and Newman clustering
This is an application that can also be fetched from https://sourceforge.net/projects/dlib.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.