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  48. <li class="toctree-l2"><a class="reference internal" href="#getting-started">Getting Started</a><ul>
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  49. <li class="toctree-l3"><a class="reference internal" href="#quick-start-notebook">Quick Start Notebook</a></li>
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    51. 

  51. </ul>
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  52. </li>
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  53. <li class="toctree-l2"><a class="reference internal" href="#installation-methods">Installation Methods</a><ul>
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  59. <li class="toctree-l2"><a class="reference internal" href="#computer-vision-models-pretrained-checkpoints">Computer Vision Models’ Pretrained Checkpoints</a><ul>
    60. 

  60. <li class="toctree-l3"><a class="reference internal" href="#pretrained-classification-pytorch-checkpoints">Pretrained Classification PyTorch Checkpoints</a></li>
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  73. <ul>
    74. 

  74. <li class="toctree-l1"><a class="reference internal" href="super_gradients.common.html">Common package</a></li>
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  75. <li class="toctree-l1"><a class="reference internal" href="super_gradients.training.html">Training package</a></li>
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  77. <p class="caption"><span class="caption-text">User Guide</span></p>
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  78. <ul>
    79. 

  79. <li class="toctree-l1"><a class="reference internal" href="user_guide.html">What is SuperGradients?</a></li>
    80. 

  80. <li class="toctree-l1"><a class="reference internal" href="user_guide.html#introducing-the-supergradients-library">Introducing the SuperGradients library</a></li>
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    82. 

  82. <li class="toctree-l1"><a class="reference internal" href="user_guide.html#integrating-your-training-code-complete-walkthrough">Integrating Your Training Code - Complete Walkthrough</a></li>
    83. 

  83. <li class="toctree-l1"><a class="reference internal" href="user_guide.html#training-parameters">Training Parameters</a></li>
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  84. <li class="toctree-l1"><a class="reference internal" href="user_guide.html#logs-and-checkpoints">Logs and Checkpoints</a></li>
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  86. <li class="toctree-l1"><a class="reference internal" href="user_guide.html#network-architectures">Network Architectures</a></li>
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  87. <li class="toctree-l1"><a class="reference internal" href="user_guide.html#pretrained-models">Pretrained Models</a></li>
    88. 

  88. <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>
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  89. <li class="toctree-l1"><a class="reference internal" href="user_guide.html#supergradients-faq">SuperGradients FAQ</a></li>
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    114. 

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    115. 

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    117. 

  117.   <img src="assets/SG_img/SG - Horizontal.png" width="600"/>
    118. 

  118.  <br/><br/>
    119. 

  119. <p><strong>Easily train or fine-tune SOTA computer vision models from one training repository.</strong></p>
    120. 

  120. <hr class="docutils" />
    121. 

  121. <p><a href="https://github.com/Deci-AI/super-gradients#prerequisites"><img src="https://img.shields.io/badge/python-3.7%20%7C%203.8%20%7C%203.9-blue" />
    122. 

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    123. 

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    124. 

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  126. <a href="https://deci-ai.github.io/super-gradients/welcome.html"><img src="https://img.shields.io/badge/docs-sphinx-brightgreen" /></p>
    127. 

  127. </div>
    128. 

  128. <section class="tex2jax_ignore mathjax_ignore" id="supergradients">
    129. 

  129. <h1>SuperGradients<a class="headerlink" href="#supergradients" title="Permalink to this headline"></a></h1>
    130. 

  130. <section id="introduction">
    131. 

  131. <h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline"></a></h2>
    132. 

  132. <p>Welcome to SuperGradients, a free open-source training library for PyTorch-based deep learning models. SuperGradients allows you to train models of any computer vision tasks or import pre-trained SOTA models, such as object detection, classification of images, and semantic segmentation for videos and images.</p>
    133. 

  133. <p>Whether you are a beginner or an expert it is likely that you already have your own training script, model, loss function implementation, etc., and thus you experienced with how difficult it is to develop a production ready deep learning model, the overhead of integrating with existing training tools with very different and stiff formats and conventions, how much effort it is to find a suitable architecture for your needs when every repo is focusing on just one task.</p>
    134. 

  134. <p>With SuperGradients you can:</p>
    135. 

  135. <ul class="simple">
    136. 

  136. <li><p>Train models for any Computer Vision task or import production-ready <a class="reference external" href="https://github.com/Deci-AI/super-gradients#pretrained-classification-pytorch-checkpoints">pre-trained SOTA models</a> (detection, segmentation, and classification - YOLOv5, DDRNet, EfficientNet, RegNet, ResNet, MobileNet, etc.)</p></li>
    137. 

  137. <li><p>Shorten the training process using tested and proven <a class="reference external" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/recipes">recipes</a> &amp; <a class="reference external" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples">code examples</a></p></li>
    138. 

  138. <li><p>Easily configure your own or use plug&amp;play training, dataset, and architecture parameters.</p></li>
    139. 

  139. <li><p>Save time and easily integrate it into your codebase.</p></li>
    140. 

  140. </ul>
    141. 

  141. <div align="center">
    142. 

  142. <img src="./assets/SG_img/detection-demo.png" width="600px">
    143. 

  143. </div>
    144. 

  144. <hr class="docutils" />
    145. 

  145. <section id="table-of-content">
    146. 

  146. <h3>Table of Content:<a class="headerlink" href="#table-of-content" title="Permalink to this headline"></a></h3>
    147. 

  147. <!-- toc -->
    148. 

  148. <ul class="simple">
    149. 

  149. <li><p><a class="reference external" href="#getting-started">Getting Started</a></p>
    150. 

  150. <ul>
    151. 

  151. <li><p><a class="reference external" href="#quick-start-notebook">Quick Start Notebook</a></p></li>
    152. 

  152. <li><p><a class="reference external" href="#supergradients-walkthrough-notebook">Walkthrough Notebook</a></p></li>
    153. 

  153. </ul>
    154. 

  154. </li>
    155. 

  155. <li><p><a class="reference external" href="#installation-methods">Installation Methods</a></p>
    156. 

  156. <ul>
    157. 

  157. <li><p><a class="reference external" href="#prerequisites">Prerequisites</a></p></li>
    158. 

  158. <li><p><a class="reference external" href="#quick-installation-of-stable-version">Quick Installation of stable version</a></p></li>
    159. 

  159. <li><p><a class="reference external" href="#installing-from-github">Installing from GitHub</a></p></li>
    160. 

  160. </ul>
    161. 

  161. </li>
    162. 

  162. <li><p><a class="reference external" href="#computer-vision-models-pretrained-checkpoints">Computer Vision Models’ Pretrained Checkpoints</a></p>
    163. 

  163. <ul>
    164. 

  164. <li><p><a class="reference external" href="#pretrained-classification-pytorch-checkpoints">Pretrained Classification PyTorch Checkpoints</a></p></li>
    165. 

  165. <li><p><a class="reference external" href="#pretrained-object-detection-pytorch-checkpoints">Pretrained Object Detection PyTorch Checkpoints</a></p></li>
    166. 

  166. <li><p><a class="reference external" href="#pretrained-semantic-segmentation-pytorch-checkpoints">Pretrained Semantic Segmentation PyTorch Checkpoints</a></p></li>
    167. 

  167. </ul>
    168. 

  168. </li>
    169. 

  169. <li><p><a class="reference external" href="#contributing">Contributing</a></p></li>
    170. 

  170. <li><p><a class="reference external" href="#citation">Citation</a></p></li>
    171. 

  171. <li><p><a class="reference external" href="#community">Community</a></p></li>
    172. 

  172. <li><p><a class="reference external" href="#license">License</a></p></li>
    173. 

  173. </ul>
    174. 

  174. <!-- tocstop -->
    175. 

  175. </section>
    176. 

  176. </section>
    177. 

  177. <section id="getting-started">
    178. 

  178. <h2>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline"></a></h2>
    179. 

  179. <section id="quick-start-notebook">
    180. 

  180. <h3>Quick Start Notebook<a class="headerlink" href="#quick-start-notebook" title="Permalink to this headline"></a></h3>
    181. 

  181. <p>Get started with our quick start notebook on Google Colab for a quick and easy start using free GPU hardware</p>
    182. 

  182. <table class="tfo-notebook-buttons" align="left">
    183. 

  183.  <td>
    184. 

  184.    <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>
    185. 

  185.  </td>
    186. 

  186.   <td>
    187. 

  187.    <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_quickstart_.ipynb"><img src="./assets/SG_img/download_logo.png" />Download notebook</a>
    188. 

  188.  </td>
    189. 

  189.  <td>
    190. 

  190.    <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>
    191. 

  191.  </td>
    192. 

  192. </table>
    193. 

  193.  </br></br>
    194. 

  194. </section>
    195. 

  195. <section id="supergradients-walkthrough-notebook">
    196. 

  196. <h3>SuperGradients Walkthrough Notebook<a class="headerlink" href="#supergradients-walkthrough-notebook" title="Permalink to this headline"></a></h3>
    197. 

  197. <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>
    198. 

  198. <table class="tfo-notebook-buttons" align="left">
    199. 

  199.  <td>
    200. 

  200.    <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>
    201. 

  201.  </td>
    202. 

  202.   <td>
    203. 

  203.    <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_Walkthrough%20.ipynb"><img src="./assets/SG_img/download_logo.png" />Download notebook</a>
    204. 

  204.  </td>
    205. 

  205.  <td>
    206. 

  206.    <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>
    207. 

  207.  </td>
    208. 

  208. </table>
    209. 

  209.  </br></br>
    210. 

  210. </section>
    211. 

  211. </section>
    212. 

  212. <section id="installation-methods">
    213. 

  213. <h2>Installation Methods<a class="headerlink" href="#installation-methods" title="Permalink to this headline"></a></h2>
    214. 

  214. <section id="prerequisites">
    215. 

  215. <h3>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline"></a></h3>
    216. 

  216. <p>General requirements:</p>
    217. 

  217. <ul class="simple">
    218. 

  218. <li><p>Python 3.7, 3.8 or 3.9 installed.</p></li>
    219. 

  219. <li><p>torch&gt;=1.9.0</p>
    220. 

  220. <ul>
    221. 

  221. <li><p>https://pytorch.org/get-started/locally/</p></li>
    222. 

  222. </ul>
    223. 

  223. </li>
    224. 

  224. <li><p>The python packages that are specified in requirements.txt;</p></li>
    225. 

  225. </ul>
    226. 

  226. <p>To train on nvidia GPUs:</p>
    227. 

  227. <ul class="simple">
    228. 

  228. <li><p><a class="reference external" href="https://developer.nvidia.com/cuda-11.2.0-download-archive?target_os=Linux&amp;target_arch=x86_64&amp;target_distro=Ubuntu">Nvidia CUDA Toolkit &gt;= 11.2</a></p></li>
    229. 

  229. <li><p>CuDNN &gt;= 8.1.x</p></li>
    230. 

  230. <li><p>Nvidia Driver with CUDA &gt;= 11.2 support (≥460.x)</p></li>
    231. 

  231. </ul>
    232. 

  232. </section>
    233. 

  233. <section id="quick-installation-of-stable-version">
    234. 

  234. <h3>Quick Installation of stable version<a class="headerlink" href="#quick-installation-of-stable-version" title="Permalink to this headline"></a></h3>
    235. 

  235. <p><strong>Not yet avilable in PyPi</strong></p>
    236. 

  236. <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>  pip install super-gradients
    237. 

  237. </pre></div>
    238. 

  238. </div>
    239. 

  239. <p>That’s it !</p>
    240. 

  240. </section>
    241. 

  241. <section id="installing-from-github">
    242. 

  242. <h3>Installing from GitHub<a class="headerlink" href="#installing-from-github" title="Permalink to this headline"></a></h3>
    243. 

  243. <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install git+https://github.com/Deci-AI/super-gradients.git@stable
    244. 

  244. </pre></div>
    245. 

  245. </div>
    246. 

  246. </section>
    247. 

  247. </section>
    248. 

  248. <section id="computer-vision-models-pretrained-checkpoints">
    249. 

  249. <h2>Computer Vision Models’ Pretrained Checkpoints<a class="headerlink" href="#computer-vision-models-pretrained-checkpoints" title="Permalink to this headline"></a></h2>
    250. 

  250. <section id="pretrained-classification-pytorch-checkpoints">
    251. 

  251. <h3>Pretrained Classification PyTorch Checkpoints<a class="headerlink" href="#pretrained-classification-pytorch-checkpoints" title="Permalink to this headline"></a></h3>
    252. 

  252. <table class="colwidths-auto docutils align-default">
    253. 

  253. <thead>
    254. 

  254. <tr class="row-odd"><th class="head"><p>Model</p></th>
    255. 

  255. <th class="head"><p>Dataset</p></th>
    256. 

  256. <th class="head"><p>Resolution</p></th>
    257. 

  257. <th class="head"><p>Top-1</p></th>
    258. 

  258. <th class="head"><p>Top-5</p></th>
    259. 

  259. <th class="head"><p>Latency b1<sub>T4</sub></p></th>
    260. 

  260. <th class="text-center head"><p>Throughout b1<sub>T4</sub></p></th>
    261. 

  261. </tr>
    262. 

  262. </thead>
    263. 

  263. <tbody>
    264. 

  264. <tr class="row-even"><td><p>EfficientNet B0</p></td>
    265. 

  265. <td><p>ImageNet</p></td>
    266. 

  266. <td><p>224x224</p></td>
    267. 

  267. <td><p>77.62</p></td>
    268. 

  268. <td><p>93.49</p></td>
    269. 

  269. <td><p><strong>1.16ms</strong></p></td>
    270. 

  270. <td class="text-center"><p><strong>862fps</strong></p></td>
    271. 

  271. </tr>
    272. 

  272. <tr class="row-odd"><td><p>RegNetY200</p></td>
    273. 

  273. <td><p>ImageNet</p></td>
    274. 

  274. <td><p>224x224</p></td>
    275. 

  275. <td><p>70.88</p></td>
    276. 

  276. <td><p>89.35</p></td>
    277. 

  277. <td><p><strong>1.07ms</strong></p></td>
    278. 

  278. <td class="text-center"><p><strong>928.3fps</strong></p></td>
    279. 

  279. </tr>
    280. 

  280. <tr class="row-even"><td><p>RegNetY400</p></td>
    281. 

  281. <td><p>ImageNet</p></td>
    282. 

  282. <td><p>224x224</p></td>
    283. 

  283. <td><p>74.74</p></td>
    284. 

  284. <td><p>91.46</p></td>
    285. 

  285. <td><p><strong>1.22ms</strong></p></td>
    286. 

  286. <td class="text-center"><p><strong>816.5fps</strong></p></td>
    287. 

  287. </tr>
    288. 

  288. <tr class="row-odd"><td><p>RegNetY600</p></td>
    289. 

  289. <td><p>ImageNet</p></td>
    290. 

  290. <td><p>224x224</p></td>
    291. 

  291. <td><p>76.18</p></td>
    292. 

  292. <td><p>92.34</p></td>
    293. 

  293. <td><p><strong>1.19ms</strong></p></td>
    294. 

  294. <td class="text-center"><p><strong>838.5fps</strong></p></td>
    295. 

  295. </tr>
    296. 

  296. <tr class="row-even"><td><p>RegNetY800</p></td>
    297. 

  297. <td><p>ImageNet</p></td>
    298. 

  298. <td><p>224x224</p></td>
    299. 

  299. <td><p>77.07</p></td>
    300. 

  300. <td><p>93.26</p></td>
    301. 

  301. <td><p><strong>1.18ms</strong></p></td>
    302. 

  302. <td class="text-center"><p><strong>841.4fps</strong></p></td>
    303. 

  303. </tr>
    304. 

  304. <tr class="row-odd"><td><p>ResNet18</p></td>
    305. 

  305. <td><p>ImageNet</p></td>
    306. 

  306. <td><p>224x224</p></td>
    307. 

  307. <td><p>70.6</p></td>
    308. 

  308. <td><p>89.64</p></td>
    309. 

  309. <td><p><strong>0.599ms</strong></p></td>
    310. 

  310. <td class="text-center"><p><strong>1669fps</strong></p></td>
    311. 

  311. </tr>
    312. 

  312. <tr class="row-even"><td><p>ResNet34</p></td>
    313. 

  313. <td><p>ImageNet</p></td>
    314. 

  314. <td><p>224x224</p></td>
    315. 

  315. <td><p>74.13</p></td>
    316. 

  316. <td><p>91.7</p></td>
    317. 

  317. <td><p><strong>0.89ms</strong></p></td>
    318. 

  318. <td class="text-center"><p><strong>1123fps</strong></p></td>
    319. 

  319. </tr>
    320. 

  320. <tr class="row-odd"><td><p>ResNet50</p></td>
    321. 

  321. <td><p>ImageNet</p></td>
    322. 

  322. <td><p>224x224</p></td>
    323. 

  323. <td><p>76.3</p></td>
    324. 

  324. <td><p>93.0</p></td>
    325. 

  325. <td><p><strong>0.94ms</strong></p></td>
    326. 

  326. <td class="text-center"><p><strong>1063fps</strong></p></td>
    327. 

  327. </tr>
    328. 

  328. <tr class="row-even"><td><p>MobileNetV3_large-150 epochs</p></td>
    329. 

  329. <td><p>ImageNet</p></td>
    330. 

  330. <td><p>224x224</p></td>
    331. 

  331. <td><p>73.79</p></td>
    332. 

  332. <td><p>91.54</p></td>
    333. 

  333. <td><p><strong>0.87ms</strong></p></td>
    334. 

  334. <td class="text-center"><p><strong>1149fps</strong></p></td>
    335. 

  335. </tr>
    336. 

  336. <tr class="row-odd"><td><p>MobileNetV3_large-300 epochs</p></td>
    337. 

  337. <td><p>ImageNet</p></td>
    338. 

  338. <td><p>224x224</p></td>
    339. 

  339. <td><p>74.52</p></td>
    340. 

  340. <td><p>91.92</p></td>
    341. 

  341. <td><p><strong>0.87ms</strong></p></td>
    342. 

  342. <td class="text-center"><p><strong>1149fps</strong></p></td>
    343. 

  343. </tr>
    344. 

  344. <tr class="row-even"><td><p>MobileNetV3_small</p></td>
    345. 

  345. <td><p>ImageNet</p></td>
    346. 

  346. <td><p>224x224</p></td>
    347. 

  347. <td><p>67.45</p></td>
    348. 

  348. <td><p>87.47</p></td>
    349. 

  349. <td><p><strong>0.75ms</strong></p></td>
    350. 

  350. <td class="text-center"><p><strong>1333fps</strong></p></td>
    351. 

  351. </tr>
    352. 

  352. <tr class="row-odd"><td><p>MobileNetV2_w1</p></td>
    353. 

  353. <td><p>ImageNet</p></td>
    354. 

  354. <td><p>224x224</p></td>
    355. 

  355. <td><p>73.08</p></td>
    356. 

  356. <td><p>91.1</p></td>
    357. 

  357. <td><p><strong>0.58ms</strong></p></td>
    358. 

  358. <td class="text-center"><p><strong>1724fps</strong></p></td>
    359. 

  359. </tr>
    360. 

  360. </tbody>
    361. 

  361. </table>
    362. 

  362. <blockquote>
    363. 

  363. <div><p><strong>NOTE:</strong> Performance measured on T4 GPU with TensorRT, using FP16 precision and batch size 1</p>
    364. 

  364. </div></blockquote>
    365. 

  365. </section>
    366. 

  366. <section id="pretrained-object-detection-pytorch-checkpoints">
    367. 

  367. <h3>Pretrained Object Detection PyTorch Checkpoints<a class="headerlink" href="#pretrained-object-detection-pytorch-checkpoints" title="Permalink to this headline"></a></h3>
    368. 

  368. <table class="colwidths-auto docutils align-default">
    369. 

  369. <thead>
    370. 

  370. <tr class="row-odd"><th class="head"><p>Model</p></th>
    371. 

  371. <th class="head"><p>Dataset</p></th>
    372. 

  372. <th class="head"><p>Resolution</p></th>
    373. 

  373. <th class="head"><p>mAP<sup>val<br>0.5:0.95</p></th>
    374. 

  374. <th class="head"><p>Latency b1<sub>T4</sub></p></th>
    375. 

  375. <th class="text-center head"><p>Throughout b64<sub>T4</sub></p></th>
    376. 

  376. </tr>
    377. 

  377. </thead>
    378. 

  378. <tbody>
    379. 

  379. <tr class="row-even"><td><p>YOLOv5 nano</p></td>
    380. 

  380. <td><p>COCO</p></td>
    381. 

  381. <td><p>640x640</p></td>
    382. 

  382. <td><p>27.7</p></td>
    383. 

  383. <td><p><strong>6.55ms</strong></p></td>
    384. 

  384. <td class="text-center"><p><strong>177.62fps</strong></p></td>
    385. 

  385. </tr>
    386. 

  386. <tr class="row-odd"><td><p>YOLOv5 small</p></td>
    387. 

  387. <td><p>COCO</p></td>
    388. 

  388. <td><p>640x640</p></td>
    389. 

  389. <td><p>37.3</p></td>
    390. 

  390. <td><p><strong>7.13ms</strong></p></td>
    391. 

  391. <td class="text-center"><p><strong>159.44fps</strong></p></td>
    392. 

  392. </tr>
    393. 

  393. <tr class="row-even"><td><p>YOLOv5 medium</p></td>
    394. 

  394. <td><p>COCO</p></td>
    395. 

  395. <td><p>640x640</p></td>
    396. 

  396. <td><p>45.2</p></td>
    397. 

  397. <td><p><strong>8.95ms</strong></p></td>
    398. 

  398. <td class="text-center"><p><strong>121.78fps</strong></p></td>
    399. 

  399. </tr>
    400. 

  400. <tr class="row-odd"><td><p>YOLOv5 large</p></td>
    401. 

  401. <td><p>COCO</p></td>
    402. 

  402. <td><p>640x640</p></td>
    403. 

  403. <td><p>48.0</p></td>
    404. 

  404. <td><p><strong>11.49ms</strong></p></td>
    405. 

  405. <td class="text-center"><p><strong>95.99fps</strong></p></td>
    406. 

  406. </tr>
    407. 

  407. </tbody>
    408. 

  408. </table>
    409. 

  409. <blockquote>
    410. 

  410. <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>
    411. 

  411. </div></blockquote>
    412. 

  412. </section>
    413. 

  413. <section id="pretrained-semantic-segmentation-pytorch-checkpoints">
    414. 

  414. <h3>Pretrained Semantic Segmentation PyTorch Checkpoints<a class="headerlink" href="#pretrained-semantic-segmentation-pytorch-checkpoints" title="Permalink to this headline"></a></h3>
    415. 

  415. <table class="colwidths-auto docutils align-default">
    416. 

  416. <thead>
    417. 

  417. <tr class="row-odd"><th class="head"><p>Model</p></th>
    418. 

  418. <th class="head"><p>Dataset</p></th>
    419. 

  419. <th class="head"><p>Resolution</p></th>
    420. 

  420. <th class="head"><p>mIoU</p></th>
    421. 

  421. <th class="head"><p>Latency b1<sub>T4</sub></p></th>
    422. 

  422. <th class="text-center head"><p>Throughout b64<sub>T4</sub></p></th>
    423. 

  423. </tr>
    424. 

  424. </thead>
    425. 

  425. <tbody>
    426. 

  426. <tr class="row-even"><td><p>DDRNet23</p></td>
    427. 

  427. <td><p>Cityscapes</p></td>
    428. 

  428. <td><p>1024x2048</p></td>
    429. 

  429. <td><p>78.65</p></td>
    430. 

  430. <td><p><strong>25.48ms</strong></p></td>
    431. 

  431. <td class="text-center"><p><strong>37.4fps</strong></p></td>
    432. 

  432. </tr>
    433. 

  433. <tr class="row-odd"><td><p>DDRNet23 slim</p></td>
    434. 

  434. <td><p>Cityscapes</p></td>
    435. 

  435. <td><p>1024x2048</p></td>
    436. 

  436. <td><p>76.6</p></td>
    437. 

  437. <td><p><strong>22.24ms</strong></p></td>
    438. 

  438. <td class="text-center"><p><strong>45.7fps</strong></p></td>
    439. 

  439. </tr>
    440. 

  440. <tr class="row-even"><td><p>ShelfNet_LW_34</p></td>
    441. 

  441. <td><p>COCO Segmentation (21 classes from PASCAL including background)</p></td>
    442. 

  442. <td><p>512x512</p></td>
    443. 

  443. <td><p>65.1</p></td>
    444. 

  444. <td><p><strong>-</strong></p></td>
    445. 

  445. <td class="text-center"><p><strong>-</strong></p></td>
    446. 

  446. </tr>
    447. 

  447. </tbody>
    448. 

  448. </table>
    449. 

  449. <blockquote>
    450. 

  450. <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>
    451. 

  451. </div></blockquote>
    452. 

  452. </section>
    453. 

  453. </section>
    454. 

  454. <section id="contributing">
    455. 

  455. <h2>Contributing<a class="headerlink" href="#contributing" title="Permalink to this headline"></a></h2>
    456. 

  456. <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>
    457. 

  457. <p>Our awesome contributors:</p>
    458. 

  458. <a href="https://github.com/Deci-AI/super-gradients/graphs/contributors">
    459. 

  459.   <img src="https://contrib.rocks/image?repo=Deci-AI/super-gradients" />
    460. 

  460. </a>
    461. 

  461. <p><br/>Made with <a class="reference external" href="https://contrib.rocks">contrib.rocks</a>.</p>
    462. 

  462. </section>
    463. 

  463. <section id="citation">
    464. 

  464. <h2>Citation<a class="headerlink" href="#citation" title="Permalink to this headline"></a></h2>
    465. 

  465. <p>If you use SuperGradients library or benchmark in your research, please cite SuperGradients deep learning training library.</p>
    466. 

  466. </section>
    467. 

  467. <section id="community">
    468. 

  468. <h2>Community<a class="headerlink" href="#community" title="Permalink to this headline"></a></h2>
    469. 

  469. <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,
    470. 

  470. or want to file a bug or issue report, we would love to welcome you aboard!</p>
    471. 

  471. <ul class="simple">
    472. 

  472. <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>
    473. 

  473. <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>
    474. 

  474. <li><p>You can also join the <a class="reference external" href="https://deci.ai/resources/blog/">community mailing list</a>
    475. 

  475. to ask questions about the project and receive announcements.</p></li>
    476. 

  476. </ul>
    477. 

  477. </section>
    478. 

  478. <section id="license">
    479. 

  479. <h2>License<a class="headerlink" href="#license" title="Permalink to this headline"></a></h2>
    480. 

  480. <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>
    481. 

  481. </section>
    482. 

  482. </section>
    483. 

  483. 

  484. 

  485.            </div>
    486. 

  486.           </div>
    487. 

  487.           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
    488. 

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  492.   <hr/>
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  494.   <div role="contentinfo">
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Use Google Cloud Storage!

Browsing data directories saved to Google Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Google Storage bucket

Service Account Key

Congratulations!

super-gradients is now integrated with Google Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate Azure Cloud Storage
Use Azure Storage
Select bucket
Set key
Finish

Use Azure Cloud Storage!

Browsing data directories saved to Azure Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Azure Storage bucket

Access key (If needed)

Congratulations!

super-gradients is now integrated with Azure Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate S3 compatible storage
Use S3 like remote
Select bucket
Access key
Finish

Use any S3 compatible storage!

Browsing data directories saved to S3 compatible storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your S3 bucket

Bucket name cannot be the same as the repository name. Please change one of them.

Access key (If needed)

Congratulations!

super-gradients is now integrated with your S3 compatible storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes