I recently started working in the field of computer vision. And in these early days, I'm studying how the various algorithms of object detection work. Among the most well-known ones are R-CNN, Fast R-CNN, Faster R-CNN and of course YOLO. In this article, I want to focus on the last mentioned algorithm. YOLO is the state of the art in object detection and there are endless use cases where YOLO can be used.

This app does object detection using the SSD Mobilenet Caffe model, the Intel Movidius Neural Compute Stick 2, OpenVINO Toolkit R3 and the Intel RealSense depth camera. It first detects an object in the video frame and then uses the depth stream to detect how far the object is using the Intel RealSense depth camera (tested with Intel RealSense D415). The default model used in this sample uses the PASCAL Voc dataset and detects up to 20 classes. Please see the networks/ssd_mobilenet_caffe sample for more information. Note: All development and testing has been done on Ubuntu 16.04 on an x86-64 machine.

This document provides a short description about producing ahead-of-time compiled executable bundles. The motivation for this work is to remove the cost of compile time by allowing the users of Glow to compile the package ahead of time. A bundle is a self-contained compiled network model that can be used to execute the model in a standalone mode. After following the instructions in this document and the Makefile in the example directory you will be able to compile convolutional neural networks into small executables. It is possible to use the Glow library to produce bundles.

StarSpace is a library for efficient learning of entity representations from relations among collections of discrete entities. In the general case, it embeds objects of different types into a vectorial embedding space, hence the star ('*') and space in the name, and in that space compares them against each other. It learns to rank a set of entities/documents or objects given a query entity/document or object, which is not necessarily the same type as the items in the set. StarSpace builds on modern Mac OS and Linux distributions. Since it uses C 11 features, it requires a compiler with good C 11 support.

Since it uses C 11 features, it requires a compiler with good C 11 support. Compilation is carried out using a Makefile, so you will need to have a working make. This will produce object files for all the classes as well as the main binary fasttext. If you do not plan on using the default system-wide compiler, update the two macros defined at the beginning of the Makefile (CC and INCLUDES). If you inted to build with Docker, a Docker file is available here fastText-Docker.

Since it uses C 11 features, it requires a compiler with good C 11 support. Compilation is carried out using a Makefile, so you will need to have a working make. This will produce object files for all the classes as well as the main binary fasttext. If you do not plan on using the default system-wide compiler, update the two macros defined at the beginning of the Makefile (CC and INCLUDES). If you inted to build with Docker, a Docker file is available here fastText-Docker.

TensorFlow, Google's open source library for machine learning, is now backing Apple's iOS mobile platform. While TensorFlow already has been available for Android, version 0.9, revealed this week, accommodates both iOS and the Raspberry Pi hardware platform for the internet of things. "To build TensorFlow on iOS, we've created a set of scripts, including a makefile, to drive the cross-compilation process," said Pete Warden, Google software engineer. "The makefile can also help you build TensorFlow without using [the Bazel build tool], which is not always available." Mobile capabilities in TensorFlow have been critical, Warden explained.