Displaying 1 to 5 from 5 results

cnn-models - ImageNet pre-trained models with batch normalization for the Caffe framework

  •    Python

This repository contains convolutional neural network (CNN) models trained on ImageNet by Marcel Simon at the Computer Vision Group Jena (CVGJ) using the Caffe framework as published in the accompanying technical report. Each model is in a separate subfolder and contains everything needed to reproduce the results. This repository focuses currently contains the batch-normalization-variants of AlexNet and VGG19 as well as the training code for Residual Networks (Resnet). No mean subtraction is required for the pre-trained models! We have a batch-normalization layer which basically does the same.

ResNetCAM-keras - Keras implementation of a ResNet-CAM model

  •    Python

The original Matlab implementation and paper (for AlexNet, GoogLeNet, and VGG16) can be found here. A Keras implementation of VGG-CAM can be found here. This implementation is written in Keras and uses ResNet-50, which was not explored in the original paper.

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 .

art - Exploring the connections between artworks with deep "Visual Analogies"

  •    TypeScript

Art is one of the few languages which transcends barriers of country, culture, and time. We aim to create an algorithm that can help discover the common semantic elements of art even between any culture, media, artist, or collection within the combined artworks of The Metropolitan Museum of Art and The Rijksmusem. Image retrieval systems allow individuals to find images that are semantically similar to a query image. This serves as the backbone of reverse image search engines and many product recommendation engines. We present a novel method for specializing image retrieval systems called conditional image retrieval. When applied over large art datasets, conditional image retrieval provides visual analogies that bring to light hidden connections among different artists, cultures, and media. Conditional image retrieval systems can efficiently find shared semantics between works of vastly different media and cultural origin. Our paper introduces new variants of K-Nearest Neighbor algorithms that support specializing to particular subsets of image collections on the fly.

We have large collection of open source products. Follow the tags from Tag Cloud >>

Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.