Leather Tie Up espadrilles in natural real leather. Gladitors with antislip platform and a wedge for more support
Welcome to my store where you will find simple, comfortable and absolutely stylish Greek Summer Sandals and colourful handbags all made of real leather.We offer plain leather summer flats but also beautifully decorated boho sandals. In our store you can find tie up gladiators and amazing quality bags made in classic natural colours or you can opt for our best seller strappy sandals in rose gold, silver or beige colour.
All our products are designed by me and handmade by skilled craftsmen using traditional techniques used for centuries. All items are made to order with the exception of some best sellers which we have produced in advance. All sandals are made using real leather therefore there will be variations between styles. Moreover, all sandals made of natural tan leather will age beautifully with time. They all tend to darken as you wear them or when exposed to the sun which makes them even better after every wear.
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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
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.
Features matter. The last decade of progress on various visual recognition tasks has been based considerably on the use of SIFT  and HOG . But if we look at performance on the canonical visual recognition task, PASCAL VOC object detection , 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.