You are using an outdated browser. Please upgrade your browser to improve your experience. Google Open Source Blog The latest news from Google on open source releases, events and student outreach programs. After a "close call," a coding champion Thursday, July 13, Cross-posted on The Keyword Eighteen-year-old Cameroon resident Nji Collins had just put the finishing touches on his final submission for the Google Code-In competition when his entire town lost internet access. It stayed dark for two months. This year, more than 1, entrants from 62 countries completed nearly 6, assignments. While Google sponsors and runs the contest, the participating tech organizations, who work most closely with the students, choose the winners. Those who finish the most tasks are named finalists, and the companies each select two winners from that group. Those winners are then flown to San Francisco, CA for an action-packed week involving talks at the Googleplex in Mountain View, office tours, segway journeys through the city, and a sunset cruise on the SF Bay. Lifelong friendships are formed on these trips. Mentors work with Code-In participants throughout the course of the competition to help them complete tasks and interface with the tech companies. Google Code-in winners on the Google campus Code-In also acts as an accessible introduction to computer science and the open source world. Mira Yang, a year-old from New Jersey, learned how to code for the first time this year. She says she never would have even considered studying computer science further before she dabbled in a few Code-In tasks. Now, she plans to major in it. He had been a finalist the year prior, when he was the only student from his school to compete. By Carly Schwartz, Editor-in-Chief, Google Internal News. Supercharge your Computer Vision models with the TensorFlow Object Detection API Thursday, June 15, Crossposted on the Google Research Blog At Google, we develop flexible state-of-the-art machine learning ML systems for computer vision that not only can be used to improve our products and services, but also spur progress in the research community. Creating accurate ML models capable of localizing and identifying multiple objects in a single image remains a core challenge in the field, and we invest a significant amount of time training and experimenting with these systems. Detected objects in a sample image from the COCO dataset made by one of our models. Michael Miley , original image Last October, our in-house object detection system achieved new state-of-the-art results, and placed first in the COCO detection challenge. Today we are happy to make this system available to the broader research community via the TensorFlow Object Detection API. This codebase is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. Our goals in designing this system was to support state-of-the-art models while allowing for rapid exploration and research. Our first release contains the following: A selection of trainable detection models, including: A Jupyter notebook for performing out-of-the-box inference with one of our released models Convenient local training scripts as well as distributed training and evaluation pipelines via Google Cloud The SSD models that use MobileNet are lightweight, so that they can be comfortably run in real time on mobile devices. Our winning COCO submission in used an ensemble of the Faster RCNN models, which are are more computationally intensive but significantly more accurate. For more details on the performance of these models, see our CVPR paper. Are you ready to get started? Contributions to the codebase are welcome and please stay tuned for our own further updates to the framework. To get started, download the code here and try detecting objects in some of your own images using the Jupyter notebook , or training your own pet detector on Cloud ML engine! In particular we want to highlight the contributions of the following individuals: Derek Chow, Chen Sun, Menglong Zhu, Matthew Tang, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Jasper Uijlings, Viacheslav Kovalevskyi, Kevin Murphy Also special thanks to: A Large High-Precision Human-Annotated Data Set for Object Detection in Video , Real et al. Top-Down Modulation for Object Detection , Shrivastava et al. A Video Dataset of Spatio-temporally Localized Atomic Visual Actions , Gu et al. Efficient convolutional neural networks for mobile vision applications , Howard et al. Open Source Models for Efficient On-Device Vision Wednesday, June 14, Crossposted on the Google Research Blog Deep learning has fueled tremendous progress in the field of computer vision in recent years, with neural networks repeatedly pushing the frontier of visual recognition technology. While many of those technologies such as object, landmark, logo and text recognition are provided for internet-connected devices through the Cloud Vision API , we believe that the ever-increasing computational power of mobile devices can enable the delivery of these technologies into the hands of our users, anytime, anywhere, regardless of internet connection. However, visual recognition for on device and embedded applications poses many challenges — models must run quickly with high accuracy in a resource-constrained environment making use of limited computation, power and space. Today we are pleased to announce the release of MobileNets , a family of mobile-first computer vision models for TensorFlow , designed to effectively maximize accuracy while being mindful of the restricted resources for an on-device or embedded application. MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception , are used. Example use cases include detection, fine-grain classification, attributes and geo-localization. This release contains the model definition for MobileNets in TensorFlow using TF-Slim , as well as 16 pre-trained ImageNet classification checkpoints for use in mobile projects of all sizes. The models can be run efficiently on mobile devices with TensorFlow Mobile. Operation Rosehub Introducing Cartographer A New Home for Google Open Source Introducing Python Fire, a library for automatically generating command line interfaces. Follow our student programs: Google Summer of Code Google Code-in. Collins with some of the other winners from Google Code-in Michael Miley , original image. Choose the right MobileNet model to fit your latency and size budget. The size of the network in memory and on disk is proportional to the number of parameters. The latency and power usage of the network scales with the number of Multiply-Accumulates MACs which measures the number of fused Multiplication and Addition operations. Top-1 and Top-5 accuracies are measured on the ILSVRC dataset.
Как пройти тест сонди с хорошим результатом
Инструкция по ремонту kia ceed
Взаимность чувств стихи
Как начать разговор с девушкой в соц
Дмитрий сухарев стихи
Джейсон крипипаста история
Как сделать штангу из колес от автомобиля
Welcome to detroit перевод
Стиху лукоморья дуб зеленыйна русском
Значение цифр на часах 21 21
Залог понятие и виды
7 of Google's coolest open source projects released in 2016
Игра история игрушек лего
Нашли нового инвестора на жк раменский
Расписание электричек выхино красково
Эналаприл нл 20 инструкция по применению
Курсы компьютерной графики в нижнем новгороде
Google собрала все свои Open Source проекты на одном сайте
Попало масло на диск сцепления что делать
Расписание электричек москва можайск на субботу
Bosch pbh 2800 re инструкция
Обществознание гражданское общество и правовое государство тест