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Handmade Sandals Alice etsy Flats Leather Sandals by Black tamar shalem Flat sandals on IqXwZAaWxU Handmade Sandals Alice etsy Flats Leather Sandals by Black tamar shalem Flat sandals on IqXwZAaWxU Handmade Sandals Alice etsy Flats Leather Sandals by Black tamar shalem Flat sandals on IqXwZAaWxU Handmade Sandals Alice etsy Flats Leather Sandals by Black tamar shalem Flat sandals on IqXwZAaWxU
Alice super comfortable handmade leather flats with a 1 cm heel.



Those are perfect for an effortless stylish urban look.



Easy to dress up or dress down, and perfect with jeans, dresses and skirts.



This design has a side buckle, and flatters different types of feet.







Materials:



Upper and inner: 100% high quality genuine leather.



Sole: neolite







Soles measurements:







36 EU - sole length 24 cm / 9.4 inch // sole width 8 cm / 3.1 inch



37 EU - sole length 24.5 cm / 9.6 inch // sole width 8.5 cm / 3.3 inch



38 EU - sole length 25 cm / 9.8 inch // sole width 8.5 cm / 3.3 inch



39 EU - sole length 25.5 cm / 10 inch // sole width 8.5 cm / 3.3 inch



40 EU - sole length 26 cm / 10.2 inch // sole width 9 cm / 3.5 inch



41 EU - sole length 26.5 cm / 10.4 inch // sole width 9 cm / 3.5 inch



42 EU - sole length 27 cm / 10.6 inch // sole width 9 cm / 3.5











Easy size converter:







42 41 40 39 38 37 36 35 EUROPE



10 9.5 9 8.5 7.5 6.5 6 5 USA



8.5 8 7.5 7 6 5 4.5 3.5 AUSTRALIA



7.5 7 6.5 6 5 4 3.5 2.5 UK







To ensure the right size is provided to you, please use the length measurements I have provided.



You are more than welcome to contact me with any question you may have.you can see more designs here:







HOW TO PICK YOUR SIZE?



Length measurements are found in any of my product`s descriptions.



Measuring yourself takes only a few moments, and can really help you in choosing the right size for you.



General rules: If the shoes are closed (anything you would wear with socks) the measurements would be: your foot length + about 1 cm (or 0.4 inches)



If you are choosing a sandal or a peep toe size (anything that is opened in the front), the measurements would be: your foot length + about 0.5 cm (or 0.2 inches).



HOW TO MEASURE:



place a ruler of a measuring tape on the floor, so that "0" touches a wall. Take off tour shoes and socks and stand on the ruler, with your heels pressed against the wall.Measure to your largest toe.







Black sandals, Alice, Flats, Leather Sandals, Handmade, Flat Sandals by tamar shalem on etsy
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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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Handmade Sandals Alice etsy Flats Leather Sandals by Black tamar shalem Flat sandals on IqXwZAaWxU

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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