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<section id="yolort-models">
<span id="models"></span><h1 id="models--page-root">yolort.models<a class="headerlink" href="#models--page-root" title="Permalink to this headline">¶</a></h1>
<section id="models-structure">
<h2 id="models-structure">Models structure<a class="headerlink" href="#models-structure" title="Permalink to this headline">¶</a></h2>
<p>The models expect a list of <code class="docutils literal notranslate"><span class="pre">Tensor[C,</span> <span class="pre">H,</span> <span class="pre">W]</span></code>, in the range <code class="docutils literal notranslate"><span class="pre">0-1</span></code>.
The models internally resize the images but the behaviour varies depending
on the model. Check the constructor of the models for more information.</p>
<dl class="py function">
<dt class="sig sig-object py" id="yolort.models.YOLOv5">
<span class="sig-prename descclassname"><span class="pre">yolort.models.</span></span><span class="sig-name descname"><span class="pre">YOLOv5</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arch</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.nn.modules.module.Module</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_classes</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">80</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pretrained</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">(640,</span> <span class="pre">640)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_divisible</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fixed_shape</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_color</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">114</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">→</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="_modules/yolort/models/yolov5.html#YOLOv5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#yolort.models.YOLOv5" title="Permalink to this definition">¶</a></dt>
<dd><p>Wrapping the pre-processing (<cite>LetterBox</cite>) into the YOLO models.</p>
<p>The input to the model is expected to be a list of tensors, each of shape [C, H, W], one for each
image, and should be in 0-1 range. Different images can have different sizes but they will be resized
to a fixed size that maintains the aspect ratio before passing it to the backbone.</p>
<p>The behavior of the model changes depending if it is in training or evaluation mode.</p>
<p>During training, the model expects both the input tensors, as well as a targets (list of dictionary),
containing:</p>
<blockquote>
<div><ul class="simple">
<li><p>boxes (<code class="docutils literal notranslate"><span class="pre">FloatTensor[N,</span> <span class="pre">4]</span></code>): the ground-truth boxes in <code class="docutils literal notranslate"><span class="pre">[x1,</span> <span class="pre">y1,</span> <span class="pre">x2,</span> <span class="pre">y2]</span></code> format, with
<code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre"><=</span> <span class="pre">x1</span> <span class="pre"><</span> <span class="pre">x2</span> <span class="pre"><=</span> <span class="pre">W</span></code> and <code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre"><=</span> <span class="pre">y1</span> <span class="pre"><</span> <span class="pre">y2</span> <span class="pre"><=</span> <span class="pre">H</span></code>.</p></li>
<li><p>labels (Int64Tensor[N]): the class label for each ground-truth box</p></li>
</ul>
</div></blockquote>
<p>The model returns a Dict[Tensor] during training, containing the classification and regression
losses.</p>
<p>During inference, the model requires only the input tensors, and returns the post-processed
predictions as a List[Dict[Tensor]], one for each input image. The fields of the Dict are as
follows, where <code class="docutils literal notranslate"><span class="pre">N</span></code> is the number of detections:</p>
<blockquote>
<div><ul class="simple">
<li><p>boxes (<code class="docutils literal notranslate"><span class="pre">FloatTensor[N,</span> <span class="pre">4]</span></code>): the predicted boxes in <code class="docutils literal notranslate"><span class="pre">[x1,</span> <span class="pre">y1,</span> <span class="pre">x2,</span> <span class="pre">y2]</span></code> format, with
<code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre"><=</span> <span class="pre">x1</span> <span class="pre"><</span> <span class="pre">x2</span> <span class="pre"><=</span> <span class="pre">W</span></code> and <code class="docutils literal notranslate"><span class="pre">0</span> <span class="pre"><=</span> <span class="pre">y1</span> <span class="pre"><</span> <span class="pre">y2</span> <span class="pre"><=</span> <span class="pre">H</span></code>.</p></li>
<li><p>labels (Int64Tensor[N]): the predicted labels for each detection</p></li>
<li><p>scores (Tensor[N]): the scores for each detection</p></li>
</ul>
</div></blockquote>
<p class="rubric">Example</p>
<p>Demo pipeline for YOLOv5 Inference.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">yolort.models</span> <span class="kn">import</span> <span class="n">YOLOv5</span>
<span class="c1"># Load the yolov5s version 6.0 models</span>
<span class="n">arch</span> <span class="o">=</span> <span class="s1">'yolov5_darknet_pan_s_r60'</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">YOLOv5</span><span class="p">(</span><span class="n">arch</span><span class="o">=</span><span class="n">arch</span><span class="p">,</span> <span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">score_thresh</span><span class="o">=</span><span class="mf">0.35</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="c1"># Perform inference on an image file</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="s1">'bus.jpg'</span><span class="p">)</span>
<span class="c1"># Perform inference on a list of image files</span>
<span class="n">predictions2</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">([</span><span class="s1">'bus.jpg'</span><span class="p">,</span> <span class="s1">'zidane.jpg'</span><span class="p">])</span>
</pre></div>
</div>
<p>We also support loading the custom checkpoints trained from ultralytics/yolov5</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">yolort.models</span> <span class="kn">import</span> <span class="n">YOLOv5</span>
<span class="c1"># Your trained checkpoint from ultralytics</span>
<span class="n">checkpoint_path</span> <span class="o">=</span> <span class="s1">'yolov5n.pt'</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">YOLOv5</span><span class="o">.</span><span class="n">load_from_yolov5</span><span class="p">(</span><span class="n">checkpoint_path</span><span class="p">,</span> <span class="n">score_thresh</span><span class="o">=</span><span class="mf">0.35</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
<span class="c1"># Perform inference on an image file</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="s1">'bus.jpg'</span><span class="p">)</span>
</pre></div>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>arch</strong> (<em>string</em>) – YOLO model architecture. Default: None</p></li>
<li><p><strong>model</strong> (<em>nn.Module</em>) – YOLO model. Default: None</p></li>
<li><p><strong>num_classes</strong> (<em>int</em>) – number of output classes of the model (doesn’t including
background). Default: 80</p></li>
<li><p><strong>pretrained</strong> (<em>bool</em>) – If true, returns a model pre-trained on COCO train2017</p></li>
<li><p><strong>progress</strong> (<em>bool</em>) – If True, displays a progress bar of the download to stderr</p></li>
<li><p><strong>size</strong> – (Tuple[int, int]): the minimum and maximum size of the image to be rescaled.
Default: (640, 640)</p></li>
<li><p><strong>size_divisible</strong> (<em>int</em>) – stride of the models. Default: 32</p></li>
<li><p><strong>fixed_shape</strong> (<em>Tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em><em>, </em><em>optional</em>) – Padding mode for letterboxing. If set to <cite>True</cite>,
the image will be padded to shape <cite>fixed_shape</cite> if specified. Instead the image will
be padded to a minimum rectangle to match <cite>min_size / max_size</cite> and each of its edges
is divisible by <cite>size_divisible</cite> if it is not specified. Default: None</p></li>
<li><p><strong>fill_color</strong> (<em>int</em>) – fill value for padding. Default: 114</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</section>
<section id="pre-trained-weights">
<h2 id="pre-trained-weights">Pre-trained weights<a class="headerlink" href="#pre-trained-weights" title="Permalink to this headline">¶</a></h2>
<p>The pre-trained models return the predictions of the following classes:</p>
<blockquote>
<div><div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">COCO_INSTANCE_CATEGORY_NAMES</span> <span class="o">=</span> <span class="p">[</span>
<span class="s1">'person'</span><span class="p">,</span> <span class="s1">'bicycle'</span><span class="p">,</span> <span class="s1">'car'</span><span class="p">,</span> <span class="s1">'motorcycle'</span><span class="p">,</span> <span class="s1">'airplane'</span><span class="p">,</span> <span class="s1">'bus'</span><span class="p">,</span>
<span class="s1">'train'</span><span class="p">,</span> <span class="s1">'truck'</span><span class="p">,</span> <span class="s1">'boat'</span><span class="p">,</span> <span class="s1">'traffic light'</span><span class="p">,</span> <span class="s1">'fire hydrant'</span><span class="p">,</span> <span class="s1">'stop sign'</span><span class="p">,</span>
<span class="s1">'parking meter'</span><span class="p">,</span> <span class="s1">'bench'</span><span class="p">,</span> <span class="s1">'bird'</span><span class="p">,</span> <span class="s1">'cat'</span><span class="p">,</span> <span class="s1">'dog'</span><span class="p">,</span> <span class="s1">'horse'</span><span class="p">,</span> <span class="s1">'sheep'</span><span class="p">,</span> <span class="s1">'cow'</span><span class="p">,</span>
<span class="s1">'elephant'</span><span class="p">,</span> <span class="s1">'bear'</span><span class="p">,</span> <span class="s1">'zebra'</span><span class="p">,</span> <span class="s1">'giraffe'</span><span class="p">,</span> <span class="s1">'backpack'</span><span class="p">,</span> <span class="s1">'umbrella'</span><span class="p">,</span>
<span class="s1">'handbag'</span><span class="p">,</span> <span class="s1">'tie'</span><span class="p">,</span> <span class="s1">'suitcase'</span><span class="p">,</span> <span class="s1">'frisbee'</span><span class="p">,</span> <span class="s1">'skis'</span><span class="p">,</span> <span class="s1">'snowboard'</span><span class="p">,</span> <span class="s1">'sports ball'</span><span class="p">,</span>
<span class="s1">'kite'</span><span class="p">,</span> <span class="s1">'baseball bat'</span><span class="p">,</span> <span class="s1">'baseball glove'</span><span class="p">,</span> <span class="s1">'skateboard'</span><span class="p">,</span> <span class="s1">'surfboard'</span><span class="p">,</span>
<span class="s1">'tennis racket'</span><span class="p">,</span> <span class="s1">'bottle'</span><span class="p">,</span> <span class="s1">'wine glass'</span><span class="p">,</span> <span class="s1">'cup'</span><span class="p">,</span> <span class="s1">'fork'</span><span class="p">,</span> <span class="s1">'knife'</span><span class="p">,</span> <span class="s1">'spoon'</span><span class="p">,</span> <span class="s1">'bowl'</span><span class="p">,</span>
<span class="s1">'banana'</span><span class="p">,</span> <span class="s1">'apple'</span><span class="p">,</span> <span class="s1">'sandwich'</span><span class="p">,</span> <span class="s1">'orange'</span><span class="p">,</span> <span class="s1">'broccoli'</span><span class="p">,</span> <span class="s1">'carrot'</span><span class="p">,</span> <span class="s1">'hot dog'</span><span class="p">,</span> <span class="s1">'pizza'</span><span class="p">,</span>
<span class="s1">'donut'</span><span class="p">,</span> <span class="s1">'cake'</span><span class="p">,</span> <span class="s1">'chair'</span><span class="p">,</span> <span class="s1">'couch'</span><span class="p">,</span> <span class="s1">'potted plant'</span><span class="p">,</span> <span class="s1">'bed'</span><span class="p">,</span> <span class="s1">'dining table'</span><span class="p">,</span>
<span class="s1">'toilet'</span><span class="p">,</span> <span class="s1">'tv'</span><span class="p">,</span> <span class="s1">'laptop'</span><span class="p">,</span> <span class="s1">'mouse'</span><span class="p">,</span> <span class="s1">'remote'</span><span class="p">,</span> <span class="s1">'keyboard'</span><span class="p">,</span> <span class="s1">'cell phone'</span><span class="p">,</span>
<span class="s1">'microwave'</span><span class="p">,</span> <span class="s1">'oven'</span><span class="p">,</span> <span class="s1">'toaster'</span><span class="p">,</span> <span class="s1">'sink'</span><span class="p">,</span> <span class="s1">'refrigerator'</span><span class="p">,</span> <span class="s1">'book'</span><span class="p">,</span> <span class="s1">'clock'</span><span class="p">,</span> <span class="s1">'vase'</span><span class="p">,</span>
<span class="s1">'scissors'</span><span class="p">,</span> <span class="s1">'teddy bear'</span><span class="p">,</span> <span class="s1">'hair drier'</span><span class="p">,</span> <span class="s1">'toothbrush'</span>
<span class="p">]</span>
</pre></div>
</div>
</div></blockquote>
<dl class="py function">
<dt class="sig sig-object py" id="yolort.models.yolov5n">
<span class="sig-prename descclassname"><span class="pre">yolort.models.</span></span><span class="sig-name descname"><span class="pre">yolov5n</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upstream_version</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'r6.0'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_friendly</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/yolort/models.html#yolov5n"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#yolort.models.yolov5n" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>upstream_version</strong> (<em>str</em>) – model released by the upstream YOLOv5. Possible values
are [“r6.0”]. Default: “r6.0”.</p></li>
<li><p><strong>export_friendly</strong> (<em>bool</em>) – Deciding whether to use (ONNX/TVM) export friendly mode.
Default: False.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="yolort.models.yolov5n6">
<span class="sig-prename descclassname"><span class="pre">yolort.models.</span></span><span class="sig-name descname"><span class="pre">yolov5n6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upstream_version</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'r6.0'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_friendly</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/yolort/models.html#yolov5n6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#yolort.models.yolov5n6" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>upstream_version</strong> (<em>str</em>) – model released by the upstream YOLOv5. Possible values
are [“r6.0”]. Default: “r6.0”.</p></li>
<li><p><strong>export_friendly</strong> (<em>bool</em>) – Deciding whether to use (ONNX/TVM) export friendly mode.
Default: False.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="yolort.models.yolov5s">
<span class="sig-prename descclassname"><span class="pre">yolort.models.</span></span><span class="sig-name descname"><span class="pre">yolov5s</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upstream_version</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'r6.0'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_friendly</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/yolort/models.html#yolov5s"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#yolort.models.yolov5s" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>upstream_version</strong> (<em>str</em>) – model released by the upstream YOLOv5. Possible values
are [“r3.1”, “r4.0”, “r6.0”]. Default: “r6.0”.</p></li>
<li><p><strong>export_friendly</strong> (<em>bool</em>) – Deciding whether to use (ONNX/TVM) export friendly mode.
Default: False.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="yolort.models.yolov5s6">
<span class="sig-prename descclassname"><span class="pre">yolort.models.</span></span><span class="sig-name descname"><span class="pre">yolov5s6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upstream_version</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'r6.0'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_friendly</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/yolort/models.html#yolov5s6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#yolort.models.yolov5s6" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>upstream_version</strong> (<em>str</em>) – model released by the upstream YOLOv5. Possible values
are [“r6.0”]. Default: “r6.0”.</p></li>
<li><p><strong>export_friendly</strong> (<em>bool</em>) – Deciding whether to use (ONNX/TVM) export friendly mode.
Default: False.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="yolort.models.yolov5m">
<span class="sig-prename descclassname"><span class="pre">yolort.models.</span></span><span class="sig-name descname"><span class="pre">yolov5m</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upstream_version</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'r6.0'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_friendly</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/yolort/models.html#yolov5m"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#yolort.models.yolov5m" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>upstream_version</strong> (<em>str</em>) – model released by the upstream YOLOv5. Possible values
are [“r3.1”, “r4.0”, “r6.0”]. Default: “r6.0”.</p></li>
<li><p><strong>export_friendly</strong> (<em>bool</em>) – Deciding whether to use (ONNX/TVM) export friendly mode.
Default: False.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="yolort.models.yolov5m6">
<span class="sig-prename descclassname"><span class="pre">yolort.models.</span></span><span class="sig-name descname"><span class="pre">yolov5m6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upstream_version</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'r6.0'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_friendly</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/yolort/models.html#yolov5m6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#yolort.models.yolov5m6" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>upstream_version</strong> (<em>str</em>) – model released by the upstream YOLOv5. Possible values
are [“r6.0”]. Default: “r6.0”.</p></li>
<li><p><strong>export_friendly</strong> (<em>bool</em>) – Deciding whether to use (ONNX/TVM) export friendly mode.
Default: False.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="yolort.models.yolov5l">
<span class="sig-prename descclassname"><span class="pre">yolort.models.</span></span><span class="sig-name descname"><span class="pre">yolov5l</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upstream_version</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'r6.0'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_friendly</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/yolort/models.html#yolov5l"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#yolort.models.yolov5l" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>upstream_version</strong> (<em>str</em>) – model released by the upstream YOLOv5. Possible values
are [“r3.1”, “r4.0”, “r6.0”]. Default: “r6.0”.</p></li>
<li><p><strong>export_friendly</strong> (<em>bool</em>) – Deciding whether to use (ONNX/TVM) export friendly mode.
Default: False.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt class="sig sig-object py" id="yolort.models.yolov5ts">
<span class="sig-prename descclassname"><span class="pre">yolort.models.</span></span><span class="sig-name descname"><span class="pre">yolov5ts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upstream_version</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'r4.0'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_friendly</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/yolort/models.html#yolov5ts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#yolort.models.yolov5ts" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>upstream_version</strong> (<em>str</em>) – model released by the upstream YOLOv5. Possible values
are “r4.0”. Default: “r4.0”.</p></li>
<li><p><strong>export_friendly</strong> (<em>bool</em>) – Deciding whether to use (ONNX/TVM) export friendly mode.
Default: False.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</section>
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