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Sandals Wedding Flats Greek Flats Party Pearl Bridal Sandals Natural Sandals Ivory Bridesmaid Sandals Sandals Ivory Lace Leather 6qF4w Sandals Wedding Flats Greek Flats Party Pearl Bridal Sandals Natural Sandals Ivory Bridesmaid Sandals Sandals Ivory Lace Leather 6qF4w Sandals Wedding Flats Greek Flats Party Pearl Bridal Sandals Natural Sandals Ivory Bridesmaid Sandals Sandals Ivory Lace Leather 6qF4w Sandals Wedding Flats Greek Flats Party Pearl Bridal Sandals Natural Sandals Ivory Bridesmaid Sandals Sandals Ivory Lace Leather 6qF4w Sandals Wedding Flats Greek Flats Party Pearl Bridal Sandals Natural Sandals Ivory Bridesmaid Sandals Sandals Ivory Lace Leather 6qF4w
A pair of ivory bridal sandals, made by greek natural leather.

They are decorated with ivory lace,ribbons and real natural pearls.

They are very comfortable having a rubber durable sole of the best quality.



Sandal Color: Genuine High-Quality Greek Leather on natural hue.

The leather is natural color and it may vary from light beige to light brown.

It depends on the particular hide and will darken a lot with time.

Each piece of this leather is unique, it often has some natural marks or light color irregularities.



Care instructions: Protect them from liquids & water and store them in a dry place.



Ideal for:

* wedding party flats

* bridal sandals

* bridesmaids sandals

* Summer women sandals

* gift for a bride or your bridesmaids



Here is the shoe conversion in EU sizes

UK Size 4 - USA Size 6 - EU Size 37

UK Size 5 - USA Size 7 - EU Size 38

UK Size 6 - USA Size 8 - EU Size 39

UK Size 7 - USA Size 9 - EU Size 40

UK Size 8 - USA Size 10 - EU Size 41

UK Size 9 - USA Size 11 - EU Size 42



I ship International First Priority Registered Mail to International buyers with TRACKING NUMBER.



Time of delivery:

-Greece : up to 3 days

-Europe: up to 10 days

-US : 2-3 weeks

-Rest of the world: 3-4 weeks



Every item is very carefully packed and gift wrapped.



Check here for more sandals :



Feel free to convo me anytime for any question.



Thank you for visiting!
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Womens Kids Gift Hockey Trainers Mens White Shoes Running Collector Vegas Sneakers Black Sizes Gift Custom Golden Knights zaqxEPT
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Sandals Wedding Flats Greek Flats Party Pearl Bridal Sandals Natural Sandals Ivory Bridesmaid Sandals Sandals Ivory Lace Leather 6qF4w

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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