Introduction: This challenge evaluates algorithms for object detection and image classification at large scale. This year there will be two competitions: A PASCAL-style detection challenge on fully labeled data for 200 categories of objects, and An image classification plus object localization challenge with 1000 categories. NEW: This year all participants are encouraged to submit object localization results; in past challenges, submissions to classification and classification with localization tasks were accepted separately.One high level motivation is to allow researchers to compare progress in detection across a wider variety of objects -- taking advantage of the quite expensive labeling effort. Another motivation is to measure the progress of computer vision for large scale image indexing for retrieval and annotation... ( rules and results ) New York Times article: Started in 2010 by Stanford, Princeton and Columbia University scientists, the Large Scale Visual Recognition Challenge this year drew 38 entrants from 13 countries. The groups use advanced software, in most cases modeled loosely on the biological vision systems, to detect, locate and classify a huge set of images taken from Internet sources like Twitter. The contest was sponsored this year by Google, Stanford, Facebook and the University of North Carolina. Contestants run their recognition programs on high-performance computers based in many cases on specialized processors called G.P.U.s, for graphic processing units. This year there were six categories based on object detection, locating objects and classifying them... ( cont'd )
Features ( $192 ): Tegra K1 SOC Kepler GPU with 192 CUDA cores 4-Plus-1 quad-core ARM Cortex A15 CPU 2 GB x16 memory with 64 bit width 16 GB 4.51 eMMC memory 1 Half mini-PCIE slot 1 Full size SD/MMC connector 1 Full-size HDMI port 1 USB 2.0 port, micro AB 1 USB 3.0 port, A 1 RS232 serial port 1 ALC5639 Realtek Audio codec with Mic in and Line out 1 RTL8111GS Realtek GigE LAN 1 SATA data port SPI 4MByte boot flash Dr. Dobbs has an in depth look here .
From Medium: Today, Antoine Cully at the Sorbonne University in Paris and a couple of pals say they’ve developed a technique that allows a damaged robot to learn how to walk again in just a few seconds. They say their work has important consequences for the reliability and robustness of future robots and may also provide some insight into the way that animals adapt to injury as well... ( cont'd )
Relaxing summertime viewing of a 80s era Kuka robot being taken apart and dissected ( 20 parts total ).
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