Displaying 1 to 8 from 8 results

ImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

  •    Python

A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.

Hello-AI - AI, Tensorflow, Inceptionv3, AI as a Service, Flask

  •    Python

If you want to custom the AI, you just need to add/modify the training dataset, or change the training model. We are using Flask Framework to public AI as a Service via Web Interface.

mace-models - Mobile AI Compute Engine Model Zoo

  •    

This project hosts Mobile AI Compute Engine (MACE) models. Each yml deployment script describes a case of deployments, which will generate one or one group (in case more than one ABIs specified) of static libraries and headers. To learn how to add new models, please refer to MACE documents.




kubecon-ml - 🎱 A demonstration of existing machine learning toolkits on Kubernetes

  •    Python

This repo serves as an example to demonstrate the typical machine learning workflow and how to leverage existing machine learning toolkits for Kubernetes to enhance the development and operations lifecycle. This example is based on the Tensorflow Image Retraining example.

InceptionVisionDemo - 🎥 iOS11 demo application for dominant objects detection.

  •    Swift

A Demo application using Vision and CoreML frameworks to detect the dominant objects presented in a live video feed from a set of 1000 categories such as trees, animals, food, vehicles, people, and more. This demo uses "Inception v3" CoreML model.

tensorflow-docker-retrain - Retraining of InceptionV3 or MobileNet using TensorFlow and Docker.

  •    Dockerfile

tensorflow-docker-retrain uses the TensorFlow framework to retrain an existing MobileNet (or InceptionV3) classifier via Docker without ever having to install TensorFlow. You can provide your own training images from any source by creating the appropriate directory structure. This repository optionally provides the ability to split a video into individual frames that can be used to quickly gather a significant number of training images.

Model-Playgrounds - A project developed and maintained as part of the aim at bringing current capabilities in machine learning and artificial intelligence into practical use for non-programmers and average computer users

  •    Python

        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 .