100% hand stitched leather moccasins available in all sizes as we custom make them to your actual feet measurements. All our moccasin boots are hand cut, hand punched and hand stitched from the finest quality leather. No sewing machines or assembly lines are in sight. They have beautiful native American tribal fabric incorporated into their design.
These beautiful leather moccasins combine style and functionality. An enormous amount of care and energy goes into making each moccasin. They are totally unique, unbelievably comfortable, breathable and durable.
The sole is made from a layer of soft flexible foam for extra comfort combined with a layer of hard rubber for excellent grip and durability. All my soles are also 100% handmade to your specifications.
For more of our handmade moccasins boots please click on the link below:
I cannot say it enough my moccasins are totally unique as they are 100% handmade using traditional ancient techniques dating back centuries. I use no machines just very simple tools and my hands.
As my leather boots are made by my hands and not machines minor imperfections are typical features and I believe they add character and charm. If you want a perfect pair of moccasins I'm sad to say this probably isn't the shop for you. However if you want unique handmade moccasins with loads of charm and character my moccasins are perfect for you.
They are available in all sizes.
Please follow the simple instructions below when ordering:
1) Select Male or Female from the Gender drop down menu.
2) Select your regular shoe size from the Size drop down menu. Any size from 5 to 11. If you would like to order a size larger than US 11 (feet length greater than 28 cm or 11 inches) please let me know prior to ordering as there is an additional charge of $50 for these larger sizes.
3) If you are male choose your usual men's shoe size. If you are female choose your usual women's shoe size.
We make all our moccasins 100% by hand and to your exact specifications so we will most definitely have your perfect fit. Please pick what size you want and to make 100% sure we make the most accurate fit for you, please follow these steps for measuring your feet:
1. Trace the outline of your foot and remember to measure your foot without shoes on.
2. Use your pencil to draw straight lines touching the outermost points at the top, bottom, and both sides of the outline.
3. Use your ruler to measure the length from the bottom line to the top line. This is the length of your foot.
4. Measure the width of your foot by using your ruler to measure from the line on one side of your tracing, to the line on the other side.
5. Please send me these measurements in the notes to seller section at the time of purchase or simply e-mail them to me.
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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.