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12.html
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<!DOCTYPE html><html lang="zh-CN"><head><meta charset="UTF-8"><meta name="viewport" content="width=device-width,height=device-height,initial-scale=1.0"><meta name="apple-mobile-web-app-capable" content="yes"><meta http-equiv="X-UA-Compatible" content="ie=edge"><meta property="og:type" content="website"><meta name="twitter:card" content="summary"><style>@media screen{body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=next] .bespoke-marp-parent>.bespoke-marp-osc>button,body[data-bespoke-view=presenter] .bespoke-marp-presenter-container .bespoke-marp-presenter-info-container button{-webkit-tap-highlight-color:transparent;-webkit-appearance:none;-moz-appearance:none;appearance:none;background-color:transparent;border:0;color:inherit;cursor:pointer;font-size:inherit;opacity:80%;outline:none;padding:0;transition:opacity .2s linear}body[data-bespoke-view=""] .bespoke-marp-parent>.bespoke-marp-osc>button:disabled,body[data-bespoke-view=next] 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/*!
* Marp / Marpit Gaia theme.
*
* @theme gaia
* @author Yuki Hattori
*
* @auto-scaling true
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<h1>iOS智能应用开发</h1>
<p>卷积神经网络进阶应用 II</p>
</section>
</foreignObject><foreignObject width="1280" height="720" data-marpit-advanced-background="pseudo"><section class="lead" style="" data-marpit-advanced-background="pseudo" data-marpit-pagination="1" data-marpit-pagination-total="19"></section></foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="2" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="2" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;">
<h2>本节概要</h2>
<ul>
<li>卷积神经网络进阶
<ul>
<li>目标检测 (Object Detection)</li>
</ul>
</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="3" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;--marpit-advanced-background-split:45%;" data-marpit-advanced-background="background" data-marpit-advanced-background-split="right"><div data-marpit-advanced-background-container="true" data-marpit-advanced-background-direction="horizontal"><figure style="background-image:url("images/12/simple_localization.png");background-size:90%;"></figure></div></section></foreignObject><foreignObject width="55%" height="720"><section id="3" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="3" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;--marpit-advanced-background-split:45%;" data-marpit-advanced-background="content" data-marpit-advanced-background-split="right">
<h2>回顾目标检测问题</h2>
<p>识别图片中的一个目标的位置检测</p>
<p><strong>如何解决？</strong><br />
1.</p>
</section>
</foreignObject><foreignObject width="1280" height="720" data-marpit-advanced-background="pseudo"><section style="" data-marpit-advanced-background="pseudo" data-marpit-pagination="3" data-marpit-pagination-total="19"></section></foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="4" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;" data-marpit-advanced-background="background"><div data-marpit-advanced-background-container="true" data-marpit-advanced-background-direction="horizontal"><figure style="background-image:url("images/12/mobilebet_object.png");background-size:90%;"></figure></div></section></foreignObject><foreignObject width="1280" height="720"><section id="4" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="4" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;" data-marpit-advanced-background="content">
<h2>回顾目标检测模型</h2>
</section>
</foreignObject><foreignObject width="1280" height="720" data-marpit-advanced-background="pseudo"><section style="" data-marpit-advanced-background="pseudo" data-marpit-pagination="4" data-marpit-pagination-total="19"></section></foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="5" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="5" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;">
<h2>回顾目标检测模型</h2>
<p>输入：一张图片<br />
输出：目标的bounding box坐标（2个坐标点）、目标的分类结果（向量）</p>
<p><strong>如何支持一张图片中的多个目标的检测呢？</strong><br />
开脑洞的时刻...</p>
<ol>
<li>需求识别两个目标：输出4个坐标点？--- 2个bounding boxes</li>
<li>需求识别三个目标：输出6个坐标点？--- 3个bounding boxes<br />
...</li>
<li>需求识别n个目标：输出2n个坐标点？--- n个bounding boxes</li>
</ol>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="6" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="6" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;">
<h2>尝试1: 暴力的输出2n个坐标点的问题</h2>
<ul>
<li>训练时引入多目标
<ul>
<li>需要准备对应的数据集和label，实用性几乎为0</li>
</ul>
</li>
<li>不改变训练模型，实际使用时引入多目标
<ul>
<li>多目标识别最终会退化为单目标识别</li>
</ul>
</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="7" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="7" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;">
<h2>尝试2: 切分输入的图像</h2>
<p>将图像直接切分为有重叠的、不同尺寸的小图</p>
<ul>
<li>他很慢：切多少合适？</li>
<li>会很多重复识别的情况</li>
</ul>
<p>输入一张图，通过模型寻找存在目标的潜在区域（R-CNN，Faster R-CNNss）</p>
<ul>
<li>他还是很慢</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="8" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="8" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;">
<h2>理想的解决办法：Single Stage Detectors (SSD)</h2>
<p>YOLO: <s>You Only Live Once</s> You Only Look Once</p>
<p>Darknet后外接卷积层，输出一个tensor，尺寸为13x13x375</p>
<p><img src="images/12/darknet.png" alt="height:300px" style="height:300px;" /></p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="9" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="9" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;">
<h2>YOLO输出</h2>
<ul>
<li>每一个cell：32x32的像素块，因此，总共有169位置需要设置bounding box的predictor</li>
<li>每个位置包含：15个predictors，每个predictor包含snacks分类结果+box坐标值预测</li>
</ul>
<p><img src="images/12/darknet_output_sample.png" alt="height:300px" style="height:300px;" /></p>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="10" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="10" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;">
<h2>YOLO输出的后处理1</h2>
<ul>
<li>先做个算术题：13x13x15=？
<ul>
<li>2535!个predictors，每个predictors给出个bounding box的坐标值预测，无法直接用</li>
</ul>
</li>
<li>利用confidence过滤不太可能是正确的bounding boxes</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="11" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;--marpit-advanced-background-split:45%;" data-marpit-advanced-background="background" data-marpit-advanced-background-split="right"><div data-marpit-advanced-background-container="true" data-marpit-advanced-background-direction="horizontal"><figure style="background-image:url("images/12/results_before_postprocess.png");background-size:100%;"></figure></div></section></foreignObject><foreignObject width="55%" height="720"><section id="11" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="11" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;--marpit-advanced-background-split:45%;" data-marpit-advanced-background="content" data-marpit-advanced-background-split="right">
<h2>YOLO输出的后处理2</h2>
<ul>
<li>
<p>Non-maximum suppression</p>
<ul>
<li>去除重复
<ul>
<li>准则： IOU>45%</li>
</ul>
</li>
</ul>
</li>
<li>
<p>TuriCreate已内置该功能</p>
</li>
</ul>
</section>
</foreignObject><foreignObject width="1280" height="720" data-marpit-advanced-background="pseudo"><section style="" data-marpit-advanced-background="pseudo" data-marpit-pagination="11" data-marpit-pagination-total="19"></section></foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="12" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="12" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;">
<h2>稍微移步至notebook</h2>
</section>
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<h2>TinyYOLO in Core ML</h2>
<p>以Pipeline形式存在</p>
<ul>
<li>Pipeline=多个模型的融合</li>
</ul>
<p>SnackDetector包含:</p>
<ul>
<li>object detection model</li>
<li>NMS model</li>
</ul>
</section>
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<h2>SnackDetector输入输出</h2>
<p>输入：Image，iouThreshold，confidenceThreshold<br />
输出：confidence values (0... x 20) + coordinates(0... x 4)</p>
</section>
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<h2>利用VisionFramework开发demo.app</h2>
<ul>
<li>
<p>ViewController.swift:</p>
<ul>
<li>VNCoreMLModel</li>
<li>VNCoreMLRequest</li>
<li>VNImageRequestHandler</li>
</ul>
</li>
<li>
<p>流程：利用VNImageRequestHandler调用一次request，等待Vision模块的结果返回，处理返回后的结果</p>
<ul>
<li><strong>如何处理返回后的结果</strong></li>
</ul>
</li>
</ul>
</section>
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<h2>Vision家族新成员：VNRecognizedObjectObservation</h2>
<p>VNRecognizedObjectObservation可用来处理SnackDetector的结果</p>
<ul>
<li>Vision request通过completion handler调用：
<ul>
<li>processObservations(for:error:)
<ul>
<li>返回数组，其元素对象为VNRecognizedObjectObservation</li>
</ul>
</li>
</ul>
</li>
<li>processObservations调用show(predictions:)：
<ul>
<li>遍历所有的VNRecognizedObjectObservation对象，根据坐标，把bounding box画到屏幕上</li>
</ul>
</li>
</ul>
</section>
</foreignObject></svg><svg data-marpit-svg="" viewBox="0 0 1280 720"><foreignObject width="1280" height="720"><section id="17" data-paginate="true" data-background-color="#fff" data-theme="gaia" data-marpit-pagination="17" data-marpit-pagination-total="19" style="--paginate:true;--background-color:#fff;--theme:gaia;background-color:#fff;background-image:none;">
<h2>SnackDetector处理流程</h2>
<ul>
<li>VNRecognizedObjectObservation
<ul>
<li>labels：VNClassificationObservation</li>
<li>boundingBox：CGRect
<ul>
<li>注意坐标值转换</li>
</ul>
</li>
</ul>
</li>
</ul>
<p>获得label和boundingbox坐标后，交给BoundingBoxView class绘制</p>
</section>
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<h2>结果展示</h2>
</section>
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