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Bridal Heels- Personalized Wedding Peep Toe Pumps for the Bride



Fabulous heels encrusted in diamanté rhinestone beading for lots of sparkle. Choose from ivory or white in 3 heel heights: 2 inch, 3.5 inch, 4.5 inch.



The outside side of each shoe is personalized with your new last name, or your new last name and wedding date in your choice of shimmer vinyl lettering.



The bottom of the heel is wrapped in satin ribbon with a few Swarovski rhinestones for a little extra sparkle!







HOW TO ORDER



1. select the shoe color/height from the drop down box



2. select the shoe size from the next drop down box



3. click 'add to cart'



4. there will be a notes box available before checkout is complete, please write into the notes box:



-Lettering color - write the name of the color from the lettering color chart



-Last name for personalization for left shoe



-To have the date on the right shoe - specify if you want Est or est before the date. Then the date goes in format MM.DD.YY or MM/DD/YY or MM-DD-YY.



Example: For September 21, 2018 = est 09.21.18 OR est 09-21-18 OR est 09/21/18



*the name of the month will not be written out and the year will be 2 digits only



5. Complete checkout











*If lettering color is not specified, they will be made in the color of the first listing photo.







Please note that this item takes 4-5 weeks in processing and production time.



We can accomodate rush orders if your size is in stock. If you need them quickly please contact us for availability and rush pricing.



Questions? Please check the shipping & policies tabs for answers to all of your questions! If you cannot find the answer, please contact us and we are happy to help you make your selection.



Please note our business hours are from 9-3:30 M-F, so rest assured if you contact us that we will get back to you as soon as possible.
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Abstract: Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights:... View more
Abstract:
Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that typically combine multiple low-level image features with high-level context. In this paper, we propose a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%. Our approach combines two key insights: (1) one can apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals in order to localize and segment objects and (2) when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost. Since we combine region proposals with CNNs, we call our method R-CNN: Regions with CNN features. We also present experiments that provide insight into what the network learns, revealing a rich hierarchy of image features. Source code for the complete system is available at http://www.cs.berkeley.edu/~rbg/rcnn.
Date of Conference: 23-28 June 2014
Date Added to IEEE Xplore: 25 September 2014
Electronic ISBN: 978-1-4799-5118-5
ISSN Information:
INSPEC Accession Number: 14632381
Publisher: IEEE
Conference Location: Columbus, OH, USA
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Contents
Contents

1. Introduction

Features matter. The last decade of progress on various visual recognition tasks has been based considerably on the use of SIFT [27] and HOG [7]. But if we look at performance on the canonical visual recognition task, PASCAL VOC object detection [13], it is generally acknowledged that progress has been slow during 2010–2012, with small gains obtained by building ensemble systems and employing minor variants of successful methods.

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