The Machine Learning Model Playgrounds is a project that is part of the dream of
a team of Moses Olafenwa and John Olafenwa to bring current capabilities in machine learning and artificial intelligence into practical
use for non-programmers and average computer users. This project is the first step in what we hope will become
mainstream application in modern technology in which Computers, Smartphones, Edge Devices and Systems will
have in-built state-of-the-art Machine Learning and Artificial Intelligence capabilities without having to
connect to cloud based services.
The Machine Learning Model Playgrounds is a series of Windows programs built using pure
python libraries and code. Each of the programs is a user-friendly demo of Image Classification powered by
a specific image classification model of popular Machine Learning Algorithms trained on the ImageNet (1000 object classes )
dataset. Each program provides a user interface where users can select a picture from their Windows system folder
while the program process the selected picture and give top-10 possible results of the objects detected with
percentage probability per each result.
This repository contains the source code, models and builds of each of the programs in the
Model Playgrounds series. It is provided to allow other developers outside our team to adapt, modify or extend
the code to produce more programs that may be specific to a social, business, economic or scientific need.
The dependencies used for this project are listed below:
- Python 3.5.2
- Tensorflow 1.4.0
- Keras 2.0.8
- Numpy 1.13.1
- Scipy 0.19.1
- wxPython 4.0.0
Below you will find the details and pictures of each of the programs in the series.
The ResNet Playground is powered by the ResNet50 model trained
on the ImageNet dataset. You can find its source codes in the resnet-playground folder
of this repository or follow this link. You can also download the Windows Installer
for the program in the Release section of this project or follow this link.
This program is a Windows 64-bit software that can be installed on
Windows 7 and later versions of the Operating System. It has an installer size of 227mb and install
size of 690mb. The program was compiled using PyInstaller 3.3 for Python 3.5 .
The DenseNet Playground is powered by the DenseNet121 model trained
on the ImageNet dataset. You can find its source codes in the densenet-playground folder
of this repository or follow this link. You can also download the Windows Installer
for the program in the Release section of this project or follow this link.
This program is a Windows 64-bit software that can be installed on
Windows 7 and later versions of the Operating System. It has an installer size of 166mb and install
size of 623mb. The program was compiled using PyInstaller 3.3 for Python 3.5 .
The SqueezeNet Playground is powered by the SqueezeNet model trained
on the ImageNet dataset. You can find its source codes in the squeezenet-playground folder
of this repository or follow this link. You can also download the Windows Installer
for the program in the Release section of this project or follow this link.
This program is a Windows 64-bit software that can be installed on
Windows 7 and later versions of the Operating System. It has an installer size of 142mb and install
size of 596mb. The program was compiled using PyInstaller 3.3 for Python 3.5 .
The Inception Playground is powered by the Inception V3 model trained
on the ImageNet dataset. You can find its source codes in the inception-playground folder
of this repository or follow this link. You can also download the Windows Installer
for the program in the Release section of this project or follow this link.
This program is a Windows 64-bit software that can be installed on
Windows 7 and later versions of the Operating System. It has an installer size of 221mb and install
size of 686mb. The program was compiled using PyInstaller 3.3 for Python 3.5 .
We are open to comments, suggestions and questions on this project. Feel free to reach to our team via our contact below:
- Moses Olafenwa, Chief Programmer of the Playground project
Email: guymodscientist@gmail.com
Twitter: @OlafenwaMoses
- John Olafenwa, Technical Adviser of the Playground project
Email: johnolafenwa@gmail.com
Twitter: @johnolafenwa