<li class="toctree-l1"><a class="reference internal" href="user_guide.html#how-to-reproduce-our-training-recipes">How To Reproduce Our Training Recipes</a></li>
<h1>SuperGradients<a class="headerlink" href="#supergradients" title="Permalink to this headline"></a></h1>
<section id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline"></a></h2>
<p>Welcome to SuperGradients, a free, open-source training library for PyTorch-based deep learning models.
SuperGradients allows you to train or fine-tune SOTA pre-trained models for all the most commonly applied computer vision tasks with just one training library. We currently support object detection, image classification and semantic segmentation for videos and images.</p>
<p>Docs and full user guide<span class="xref myst"></span></p>
<section id="why-use-supergradients">
<h3>Why use SuperGradients?<a class="headerlink" href="#why-use-supergradients" title="Permalink to this headline"></a></h3>
<p><strong>Built-in SOTA Models</strong></p>
<p>Easily load and fine-tune production-ready, <a class="reference external" href="https://github.com/Deci-AI/super-gradients#pretrained-classification-pytorch-checkpoints">pre-trained SOTA models</a> that incorporate best practices and validated hyper-parameters for achieving best-in-class accuracy.</p>
<p>Why do all the grind work, if we already did it for you? leverage tested and proven <a class="reference external" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/recipes">recipes</a> & <a class="reference external" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples">code examples</a> for a wide range of computer vision models generated by our team of deep learning experts. Easily configure your own or use plug & play hyperparameters for training, dataset, and architecture.</p>
<p><strong>Production Readiness and Ease of Integration</strong></p>
<p>All SuperGradients models’ are production ready in the sense that they are compatible with deployment tools such as TensorRT (Nvidia) and OpenVino (Intel) and can be easily taken into production. With a few lines of code you can easily integrate the models into your codebase.</p>
<h3>Documentation<a class="headerlink" href="#documentation" title="Permalink to this headline"></a></h3>
<p>Check SuperGradients <a class="reference external" href="https://deci-ai.github.io/super-gradients/welcome.html">Docs</a> for full documentation, user guide, and examples.</p>
</section>
<hr class="docutils" />
<section id="table-of-content">
<h3>Table of Content:<a class="headerlink" href="#table-of-content" title="Permalink to this headline"></a></h3>
<h2>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline"></a></h2>
<section id="quick-start-notebook">
<h3>Quick Start Notebook<a class="headerlink" href="#quick-start-notebook" title="Permalink to this headline"></a></h3>
<p>Get started with our quick start notebook on Google Colab for a quick and easy start using free GPU hardware</p>
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/drive/12cURMPVQrvhgYle-wGmE2z8b_p90BdL0?usp=sharing"><img src="./assets/SG_img/colab_logo.png" />SuperGradients Quick Start in Google Colab</a>
<h3>SuperGradients Walkthrough Notebook<a class="headerlink" href="#supergradients-walkthrough-notebook" title="Permalink to this headline"></a></h3>
<p>Learn more about SuperGradients training components with our walkthrough notebook on Google Colab for an easy to use tutorial using free GPU hardware</p>
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/drive/1smwh4EAgE8PwnCtwsdU8a9D9Ezfh6FQK?usp=sharing"><img src="./assets/SG_img/colab_logo.png" />SuperGradients Walkthrough in Google Colab</a>
<a target="_blank" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples"><img src="./assets/SG_img/GitHub_logo.png" />View source on GitHub</a>
</td>
</table>
</br></br>
</section>
<section id="transfer-learning-with-sg-notebook">
<h3>Transfer Learning with SG Notebook<a class="headerlink" href="#transfer-learning-with-sg-notebook" title="Permalink to this headline"></a></h3>
<p>Learn more about SuperGradients transfer learning or fine tuning abilities with our COCO pre-trained YoloV5nano fine tuning into a sub-dataset of PASCAL VOC example notebook on Google Colab for an easy to use tutorial using free GPU hardware</p>
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://colab.research.google.com/drive/1ZR_cvy8tQB_fTZwB2SQxg3RfIVKxNxRO?usp=sharing"><img src="./assets/SG_img/colab_logo.png" />SuperGradients Transfer Learning in Google Colab</a>
<h3>Quick Installation of stable version<a class="headerlink" href="#quick-installation-of-stable-version" title="Permalink to this headline"></a></h3>
<p>See in <a class="reference external" href="https://pypi.org/project/super-gradients/">PyPi</a></p>
<h3>Pretrained Classification PyTorch Checkpoints<a class="headerlink" href="#pretrained-classification-pytorch-checkpoints" title="Permalink to this headline"></a></h3>
<div><p><strong>NOTE:</strong> Performance measured on T4 GPU with TensorRT, using FP16 precision and batch size 1 (latency) and batch size 64 (througput)</p>
<div><p><strong>NOTE:</strong> Performance measured on T4 GPU with TensorRT, using FP16 precision and batch size 1 (latency), and not including IO</p>
</div></blockquote>
</section>
</section>
<section id="contributing">
<h2>Contributing<a class="headerlink" href="#contributing" title="Permalink to this headline"></a></h2>
<p>To learn about making a contribution to SuperGradients, please see our <a class="reference internal" href="CONTRIBUTING.html"><span class="doc std std-doc">Contribution page</span></a>.</p>
<p><br/>Made with <a class="reference external" href="https://contrib.rocks">contrib.rocks</a>.</p>
</section>
<section id="citation">
<h2>Citation<a class="headerlink" href="#citation" title="Permalink to this headline"></a></h2>
<p>If you are using SuperGradients library or benchmarks in your research, please cite SuperGradients deep learning training library.</p>
</section>
<section id="community">
<h2>Community<a class="headerlink" href="#community" title="Permalink to this headline"></a></h2>
<p>If you want to be a part of SuperGradients growing community, hear about all the exciting news and updates, need help, request for advanced features,
or want to file a bug or issue report, we would love to welcome you aboard!</p>
<ul class="simple">
<li><p>Slack is the place to be and ask questions about SuperGradients and get support. <a class="reference external" href="https://join.slack.com/t/supergradients-comm52/shared_invite/zt-10vz6o1ia-b_0W5jEPEnuHXm087K~t8Q">Click here to join our Slack</a></p></li>
<li><p>To report a bug, <a class="reference external" href="https://github.com/Deci-AI/super-gradients/issues">file an issue</a> on GitHub.</p></li>
<li><p>You can also join the <a class="reference external" href="https://deci.ai/resources/blog/">community mailing list</a>
to ask questions about the project and receive announcements.</p></li>
<li><p>For a shorth meeting with SuperGradients PM, use this <a class="reference external" href="https://calendly.com/ofer-baratz-deci/15min">link</a> and choose your prefered time.</p></li>
</ul>
</section>
<section id="license">
<h2>License<a class="headerlink" href="#license" title="Permalink to this headline"></a></h2>
<p>This project is released under the <a class="reference internal" href="LICENSE.html"><span class="doc std std-doc">Apache 2.0 license</span></a>.</p>
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