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PRODUCT INFORMATION







Features a full front-toe canvas print.



Elastic stretch in-step for easy on-and-off use.



The leather is very soft textile lining with lightweight construction for maximum comfort and will not ulcerate your feet and at the same time does not stretch.



Extremely durable. These sandals are designed for all-day walks. You will wear them the next 3-4 years



High-quality canvas construction for everyday use and durable EVA outsole for exceptional traction



They are classic black shoes that you will wear with your elegant dress, or any look that you desire. Additionally, the warranty for all of our products is one calendar year.



They are handmade ​​with lots of love and effort especially for you



Sandals Details:



Color - Black



Upper Materials: Leather



Inner Materials: Leather.



Shoes are Soft, lightweight, manufactured sole.



Heel height -2 cm / 0.78"



Available Size :



Place your feet on a piece of paper and draw the shape of your feet.



Use the drawing and measure the distance from the heel to the big toe.



Additionally, measure the part under the toe line for the width.



Please see an example on the picture above.



Then you will find your correct shoe size in the following table:







Children shoe sizes:



US 11 (CHILD) = UK 10 = EU 28 = 18 cm



US 11.5 (CHILD) = UK 10.5 = EU 29 = 18.5 cm



US 12 (CHILD) = UK 11 = EU 30 = 19 cm



US 13 (CHILD) = UK 12 = EU 31 = 19.5 cm



US 1 (YOUTH) = UK 13 = EU 32 = 20 cm



US 2 (YOUTH) = UK 14 = EU 33 = 21 cm



US 3 (YOUTH) = UK 15 = EU 34 = 21.5 cm







Men slip - on canvas shoe :







Men US 8 = EU 40 = UK 7.5 = 260 mm = 10.2 Inch



Men US 9 = EU 41 = UK 8.5 = 266 mm = 10.4 Inch



Men US 10 = EU 42 = UK 9.5 = 273 mm= 10.7 Inch



Men US 11= EU 43 = UK 10.5 = 280 mm= 11 Inch



Men US 12= EU 44 = UK 11.5 = 286 mm = 11.2 Inch



Men US 13= EU 45 = UK 12.5 = 293 mm = 11.53 Inch



Men US 14= EU 46 = UK 13.5 = 300 mm = 11.81 Inch







Women slip - on canvas shoe :



Women US 6 = EU 36 = UK 4 = 233 mm = 9.2 Inch



Women US 7 = EU 37 = UK 5 = 240 mm = 9.4 Inch



Women US 7.5 = EU 38 = UK 5.5 = 247 mm = 9.7 Inch



Women US 8 = EU 39 = UK 6 = 253 mm = 10 Inch



Women US 9 = EU 40 = UK 7 = 260 mm = 10.2 Inch



Women US 10 = EU 41 = UK 8 = 266 mm = 10.4 Inch



Women US 11 = EU 42 = UK 9 = 273 mm = 10.7 Inch



Women US 12 = EU 43 = UK 10 = 280 mm = 11 Inch







If you are not sure which size you are, please contact me for assistance.







SHIPPING







We Offer FREE SHIPPING with all Slip-On all over the World



It takes 15-20 days to deliver with the standard shipping service and 10 days with the express-line shipping service







CUSTOM ORDERS







We do custom orders for bunions with a small addition which you can dream up, imagine - or whatever you like. Let’s describe your own idea and please contact me for more information.



we'll send you a mock up for you to approve. We can make as many changes as you want before printing.







Feel free to contact me if you have any question, I will be happy to help.



Thanks for visiting my shop.
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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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