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  1. <!DOCTYPE html>
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  44. <li class="toctree-l2"><a class="reference internal" href="#introduction">Introduction</a><ul>
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  54. <li class="toctree-l2"><a class="reference internal" href="#getting-started">Getting Started</a><ul>
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  55. <li class="toctree-l3"><a class="reference internal" href="#quick-start-notebook">Quick Start Notebook</a></li>
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  56. <li class="toctree-l3"><a class="reference internal" href="#supergradients-walkthrough-notebook">SuperGradients Walkthrough Notebook</a></li>
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  57. <li class="toctree-l3"><a class="reference internal" href="#transfer-learning-with-sg-notebook">Transfer Learning with SG Notebook</a></li>
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  60. <li class="toctree-l2"><a class="reference internal" href="#installation-methods">Installation Methods</a><ul>
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  61. <li class="toctree-l3"><a class="reference internal" href="#prerequisites">Prerequisites</a></li>
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  62. <li class="toctree-l3"><a class="reference internal" href="#quick-installation">Quick Installation</a></li>
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  65. <li class="toctree-l2"><a class="reference internal" href="#computer-vision-models-pretrained-checkpoints">Computer Vision Models’ Pretrained Checkpoints</a><ul>
    66. 

  66. <li class="toctree-l3"><a class="reference internal" href="#pretrained-classification-pytorch-checkpoints">Pretrained Classification PyTorch Checkpoints</a></li>
    67. 

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  68. <li class="toctree-l3"><a class="reference internal" href="#pretrained-semantic-segmentation-pytorch-checkpoints">Pretrained Semantic Segmentation PyTorch Checkpoints</a></li>
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  79. <p class="caption"><span class="caption-text">Technical Documentation</span></p>
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  80. <ul>
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  81. <li class="toctree-l1"><a class="reference internal" href="super_gradients.common.html">Common package</a></li>
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  82. <li class="toctree-l1"><a class="reference internal" href="super_gradients.training.html">Training package</a></li>
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  83. </ul>
    84. 

  84. <p class="caption"><span class="caption-text">User Guide</span></p>
    85. 

  85. <ul>
    86. 

  86. <li class="toctree-l1"><a class="reference internal" href="user_guide.html">What is SuperGradients?</a></li>
    87. 

  87. <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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  88. <li class="toctree-l1"><a class="reference internal" href="user_guide.html#installation">Installation</a></li>
    89. 

  89. <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>
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  90. <li class="toctree-l1"><a class="reference internal" href="user_guide.html#training-parameters">Training Parameters</a></li>
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  94. <li class="toctree-l1"><a class="reference internal" href="user_guide.html#pretrained-models">Pretrained Models</a></li>
    95. 

  95. <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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  124. <img src="assets/SG_img/SG - Horizontal.png" width="600"/>
    125. 

  125. <br/><br/>
    126. 

  126. <p><strong>Easily train or fine-tune SOTA computer vision models with one open source training library</strong>
    127. 

  127. <a class="reference external" href="https://twitter.com/intent/tweet?text=Easily%20train%20or%20fine-tune%20SOTA%20computer%20vision%20models%20from%20one%20training%20repository&amp;url=https://github.com/Deci-AI/super-gradients&amp;via=deci_ai&amp;hashtags=AI,deeplearning,computervision,training,opensource"><img alt="Tweet" src="https://img.shields.io/twitter/url/http/shields.io.svg?style=social" /></a></p>
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    130. 

  130. <a href="https://github.com/Deci-AI/super-gradients#prerequisites"><img src="https://img.shields.io/badge/pytorch-1.9%20%7C%201.10-blue" />
    131. 

  131. <a href="https://pypi.org/project/super-gradients/"><img src="https://img.shields.io/pypi/v/super-gradients" />
    132. 

  132. <a href="https://github.com/Deci-AI/super-gradients#computer-vision-models-pretrained-checkpoints" ><img src="https://img.shields.io/badge/pre--trained%20models-25-brightgreen" />
    133. 

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  135. <a href="https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.md"><img src="https://img.shields.io/badge/license-Apache%202.0-blue" />
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    137. 

  137. </div>
    138. 

  138. <section class="tex2jax_ignore mathjax_ignore" id="supergradients">
    139. 

  139. <h1>SuperGradients<a class="headerlink" href="#supergradients" title="Permalink to this headline"></a></h1>
    140. 

  140. <section id="introduction">
    141. 

  141. <h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline"></a></h2>
    142. 

  142. <p>Welcome to SuperGradients, a free, open-source training library for PyTorch-based deep learning models.
    143. 

  143. 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>
    144. 

  144. <p>Docs and full user guide<span class="xref myst"></span></p>
    145. 

  145. <section id="why-use-supergradients">
    146. 

  146. <h3>Why use SuperGradients?<a class="headerlink" href="#why-use-supergradients" title="Permalink to this headline"></a></h3>
    147. 

  147. <p><strong>Built-in SOTA Models</strong></p>
    148. 

  148. <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>
    149. 

  149. <p><strong>Easily Reproduce our Results</strong></p>
    150. 

  150. <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> &amp; <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 &amp; play hyperparameters for training, dataset, and architecture.</p>
    151. 

  151. <p><strong>Production Readiness and Ease of Integration</strong></p>
    152. 

  152. <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>
    153. 

  153. <div align="center">
    154. 

  154. <img src="./assets/SG_img/detection-demo.png" width="600px">
    155. 

  155. </div>
    156. 

  156. </section>
    157. 

  157. <section id="documentation">
    158. 

  158. 

  159. 

  160. 

  161. 

  162. 

  163. 

  164. 

  165. 

  166. 

  167. 

  168. 

  169. 

  170. 

  171. 

  172. 

  173. 

  174. 

  175. 

  176. 

  177. 

  178. <h3>Documentation<a class="headerlink" href="#documentation" title="Permalink to this headline"></a></h3>
    179. 

  179. <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>
    180. 

  180. </section>
    181. 

  181. </section>
    182. 

  182. <hr class="docutils" />
    183. 

  183. <section id="table-of-content">
    184. 

  184. <h3>Table of Content<a class="headerlink" href="#table-of-content" title="Permalink to this headline"></a></h3>
    185. 

  185. <details>
    186. 

  186. <summary>See Table </summary>
    187. 

  187. <!-- toc -->
    188. 

  188. <ul class="simple">
    189. 

  189. <li><p><a class="reference external" href="#getting-started">Getting Started</a></p>
    190. 

  190. <ul>
    191. 

  191. <li><p><a class="reference external" href="#quick-start-notebook">Quick Start Notebook</a></p></li>
    192. 

  192. <li><p><a class="reference external" href="#supergradients-walkthrough-notebook">Walkthrough Notebook</a></p></li>
    193. 

  193. <li><p><a class="reference external" href="#transfer-learning-with-sg-notebook">Transfer Learning with SG Notebook</a></p></li>
    194. 

  194. </ul>
    195. 

  195. </li>
    196. 

  196. <li><p><a class="reference external" href="#installation-methods">Installation Methods</a></p>
    197. 

  197. <ul>
    198. 

  198. <li><p><a class="reference external" href="#prerequisites">Prerequisites</a></p></li>
    199. 

  199. <li><p><a class="reference external" href="#quick-installation">Quick Installation</a></p></li>
    200. 

  200. </ul>
    201. 

  201. </li>
    202. 

  202. <li><p><a class="reference external" href="#computer-vision-models-pretrained-checkpoints">Computer Vision Models’ Pretrained Checkpoints</a></p>
    203. 

  203. <ul>
    204. 

  204. <li><p><a class="reference external" href="#pretrained-classification-pytorch-checkpoints">Pretrained Classification PyTorch Checkpoints</a></p></li>
    205. 

  205. <li><p><a class="reference external" href="#pretrained-object-detection-pytorch-checkpoints">Pretrained Object Detection PyTorch Checkpoints</a></p></li>
    206. 

  206. <li><p><a class="reference external" href="#pretrained-semantic-segmentation-pytorch-checkpoints">Pretrained Semantic Segmentation PyTorch Checkpoints</a></p></li>
    207. 

  207. </ul>
    208. 

  208. </li>
    209. 

  209. <li><p><a class="reference external" href="#contributing">Contributing</a></p></li>
    210. 

  210. <li><p><a class="reference external" href="#citation">Citation</a></p></li>
    211. 

  211. <li><p><a class="reference external" href="#community">Community</a></p></li>
    212. 

  212. <li><p><a class="reference external" href="#license">License</a></p></li>
    213. 

  213. <li><p><a class="reference external" href="#deci-lab">Deci Lab</a></p></li>
    214. 

  214. </ul>
    215. 

  215. <!-- tocstop -->
    216. 

  216. </details>
    217. 

  217. </section>
    218. 

  218. </section>
    219. 

  219. <section id="getting-started">
    220. 

  220. <h2>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this headline"></a></h2>
    221. 

  221. <section id="quick-start-notebook">
    222. 

  222. 

  223. 

  224. 

  225. 

  226. 

  227. 

  228. 

  229. 

  230. <h3>Quick Start Notebook - Classification<a class="headerlink" href="#quick-start-notebook-classification" title="Permalink to this headline"></a></h3>
    231. 

  231. <p>Get started with our quick start notebook for image classification tasks on Google Colab for a quick and easy start using free GPU hardware.</p>
    232. 

  232. <table class="tfo-notebook-buttons" align="left">
    233. 

  233. <td>
    234. 

  234. <a target="_blank" href="https://bit.ly/3ufnsgT"><img src="./assets/SG_img/colab_logo.png" />Classification Quick Start in Google Colab</a>
    235. 

  235. </td>
    236. 

  236. <td>
    237. 

  237. <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_quickstart_classification.ipynb"><img src="./assets/SG_img/download_logo.png" />Download notebook</a>
    238. 

  238. </td>
    239. 

  239. <td>
    240. 

  240. <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>
    241. 

  241. </td>
    242. 

  242. </table>
    243. 

  243. </br></br>
    244. 

  244. </section>
    245. 

  245. <section id="quick-start-notebook-semantic-segmentation">
    246. 

  246. <h3>Quick Start Notebook - Semantic Segmentation<a class="headerlink" href="#quick-start-notebook-semantic-segmentation" title="Permalink to this headline"></a></h3>
    247. 

  247. <p>Get started with our quick start notebook for semantic segmentation tasks on Google Colab for a quick and easy start using free GPU hardware.</p>
    248. 

  248. <table class="tfo-notebook-buttons" align="left">
    249. 

  249. <td>
    250. 

  250. <a target="_blank" href="https://bit.ly/3Jp7w1U"><img src="./assets/SG_img/colab_logo.png" />Segmentation Quick Start in Google Colab</a>
    251. 

  251. </td>
    252. 

  252. <td>
    253. 

  253. <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_quickstart_segmentation.ipynb"><img src="./assets/SG_img/download_logo.png" />Download notebook</a>
    254. 

  254. </td>
    255. 

  255. <td>
    256. 

  256. <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>
    257. 

  257. </td>
    258. 

  258. </table>
    259. 

  259. </br></br>
    260. 

  260. <!--
    261. 

  261. ### Quick Start Notebook - Object Detection
    262. 

  262. 

  263. Get started with our quick start notebook for object detection tasks on Google Colab for a quick and easy start using free GPU hardware.
    264. 

  264. 

  265. <table class="tfo-notebook-buttons" align="left">
    266. 

  266. <table class="tfo-notebok-buttons" align="left">
    267. 

  267. <td>
    268. 

  268. <a target="_blank" href="https://bit.ly/3wqMsEM"><img src="./docs/assets/SG_img/colab_logo.png" />Detection Quick Start in Google Colab</a>
    269. 

  269. </td>
    270. 

  270. <td>
    271. 

  271. <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_quickstart_detection.ipynb"><img src="./docs/assets/SG_img/download_logo.png" />Download notebook</a>
    272. 

  272. </td>
    273. 

  273. <td>
    274. 

  274. <a target="_blank" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples"><img src="./docs/assets/SG_img/GitHub_logo.png" />View source on GitHub</a>
    275. 

  275. </td>
    276. 

  276. </table>
    277. 

  277. </br></br>
    278. 

  278. 

  279. ### Quick Start Notebook - Upload your model to Deci Platform
    280. 

  280. 

  281. Get Started with an example of how to upload your trained model to Deci Platform for runtime optimization and compilation to your target deployment HW.
    282. 

  282. <table class="tfo-notebook-buttons" align="left">
    283. 

  283. <tbody>
    284. 

  284.  <tr>
    285. 

  285.    <td vertical-align="middle">
    286. 

  286.      <img src="./docs/assets/SG_img/colab_logo.png" />
    287. 

  287.      <a target="_blank" href="https://bit.ly/3cAkoXG">
    288. 

  288.        Upload to Deci Platform in Google Colab
    289. 

  289.      </a>
    290. 

  290.    </td>
    291. 

  291.    <td vertical-align="middle">
    292. 

  292.      <img src="./docs/assets/SG_img/download_logo.png" />
    293. 

  293.      <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_quickstart_model_upload_deci_lab.ipynb">
    294. 

  294.        Download notebook
    295. 

  295.      </a>
    296. 

  296.    </td>
    297. 

  297.    <td>
    298. 

  298.      <img src="./docs/assets/SG_img/GitHub_logo.png" />
    299. 

  299.      <a target="_blank" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/deci_lab_export_example/deci_lab_export_example.py">
    300. 

  300.        View source on GitHub
    301. 

  301.      </a>
    302. 

  302.    </td>
    303. 

  303.  </tr>
    304. 

  304. </tbody>
    305. 

  305. </table>
    306. 

  306. </br></br>
    307. 

  307. 

  308. ### SuperGradients Complete Walkthrough Notebook
    309. 

  309. 

  310. Learn more about SuperGradients training components with our walkthrough notebook on Google Colab for an easy to use tutorial using free GPU hardware
    311. 

  311. 

  312. <table class="tfo-notebook-buttons" align="left">
    313. 

  313. <td>
    314. 

  314. <a target="_blank" href="https://bit.ly/3JspSPF"><img src="./docs/assets/SG_img/colab_logo.png" />SuperGradients Walkthrough in Google Colab</a>
    315. 

  315. </td>
    316. 

  316. <td>
    317. 

  317. <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_Walkthrough.ipynb"><img src="./docs/assets/SG_img/download_logo.png" />Download notebook</a>
    318. 

  318. </td>
    319. 

  319. <td>
    320. 

  320. <a target="_blank" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples"><img src="./docs/assets/SG_img/GitHub_logo.png" />View source on GitHub</a>
    321. 

  321. </td>
    322. 

  322. </table>
    323. 

  323. </br></br>
    324. 

  324. 

  325. ### Transfer Learning with SG Notebook - Object Detection
    326. 

  326. 

  327. Learn more about SuperGradients transfer learning or fine tuning abilities with our COCO pre-trained YoloX nano fine tuning into a sub-dataset of PASCAL VOC example notebook on Google Colab for an easy to use tutorial using free GPU hardware
    328. 

  328. 

  329. <table class="tfo-notebook-buttons" align="left">
    330. 

  330. <td>
    331. 

  331. <a target="_blank" href="https://bit.ly/3iGvnP7"><img src="./docs/assets/SG_img/colab_logo.png" />Detection Transfer Learning in Google Colab</a>
    332. 

  332. </td>
    333. 

  333. <td>
    334. 

  334. <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_transfer_learning_object_detection.ipynb"><img src="./docs/assets/SG_img/download_logo.png" />Download notebook</a>
    335. 

  335. </td>
    336. 

  336. <td>
    337. 

  337. <a target="_blank" href="https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/examples"><img src="./docs/assets/SG_img/GitHub_logo.png" />View source on GitHub</a>
    338. 

  338. </td>
    339. 

  339. </table>
    340. 

  340. </br></br>
    341. 

  341. -->
    342. 

  342. </section>
    343. 

  343. </section>
    344. 

  344. <section id="transfer-learning">
    345. 

  345. <h2>Transfer Learning<a class="headerlink" href="#transfer-learning" title="Permalink to this headline"></a></h2>
    346. 

  346. <section id="transfer-learning-with-sg-notebook-semantic-segmentation">
    347. 

  347. <h3>Transfer Learning with SG Notebook - Semantic Segmentation<a class="headerlink" href="#transfer-learning-with-sg-notebook-semantic-segmentation" title="Permalink to this headline"></a></h3>
    348. 

  348. <p>Learn more about SuperGradients transfer learning or fine tuning abilities with our Citiscapes pre-trained RegSeg48 fine tuning into a sub-dataset of Supervisely example notebook on Google Colab for an easy to use tutorial using free GPU hardware</p>
    349. 

  349. <table class="tfo-notebook-buttons" align="left">
    350. 

  350. <td>
    351. 

  351. <a target="_blank" href="https://bit.ly/37P04PN"><img src="./assets/SG_img/colab_logo.png" />Segmentation Transfer Learning in Google Colab</a>
    352. 

  352. </td>
    353. 

  353. <td>
    354. 

  354. <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_transfer_learning_semantic_segmentation.ipynb"><img src="./assets/SG_img/download_logo.png" />Download notebook</a>
    355. 

  355. </td>
    356. 

  356. <td>
    357. 

  357. <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>
    358. 

  358. </td>
    359. 

  359. </table>
    360. 

  360. </br></br>
    361. 

  361. </section>
    362. 

  362. </section>
    363. 

  363. <section id="knowledge-distillation-training">
    364. 

  364. <h2>Knowledge Distillation Training<a class="headerlink" href="#knowledge-distillation-training" title="Permalink to this headline"></a></h2>
    365. 

  365. <section id="knowledge-distillation-training-quick-start-with-sg-notebook-resnet18-example">
    366. 

  366. <h3>Knowledge Distillation Training Quick Start with SG Notebook - ResNet18 example<a class="headerlink" href="#knowledge-distillation-training-quick-start-with-sg-notebook-resnet18-example" title="Permalink to this headline"></a></h3>
    367. 

  367. <p>Knowledge Distillation is a training technique that uses a large model, teacher model, to improve the performance of a smaller model, the student model.
    368. 

  368. Learn more about SuperGradients knowledge distillation training with our pre-trained BEiT base teacher model and Resnet18 student model on CIFAR10 example notebook on Google Colab for an easy to use tutorial using free GPU hardware</p>
    369. 

  369. <table class="tfo-notebook-buttons" align="left">
    370. 

  370. <td>
    371. 

  371. <a target="_blank" href="https://bit.ly/3HQvbsg"><img src="./assets/SG_img/colab_logo.png" />KD Training in Google Colab</a>
    372. 

  372. </td>
    373. 

  373. <td>
    374. 

  374. <a href="https://minhaskamal.github.io/DownGit/#/home?url=https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/examples/SG_knowledge_distillation_quickstart.ipynb"><img src="./assets/SG_img/download_logo.png" />Download notebook</a>
    375. 

  375. </td>
    376. 

  376. <td>
    377. 

  377. <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>
    378. 

  378. </td>
    379. 

  379. </table>
    380. 

  380. </br></br>
    381. 

  381. </section>
    382. 

  382. </section>
    383. 

  383. 

  384. <section id="installation-methods">
    385. 

  385. <h2>Installation Methods<a class="headerlink" href="#installation-methods" title="Permalink to this headline"></a></h2>
    386. 

  386. <section id="prerequisites">
    387. 

  387. <h3>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline"></a></h3>
    388. 

  388. <details>
    389. 

  389. <summary>General requirements</summary>
    390. 

  390. <ul class="simple">
    391. 

  391. <li><p>Python 3.7, 3.8 or 3.9 installed.</p></li>
    392. 

  392. <li><p>torch&gt;=1.9.0</p>
    393. 

  393. <ul>
    394. 

  394. <li><p>https://pytorch.org/get-started/locally/</p></li>
    395. 

  395. </ul>
    396. 

  396. </li>
    397. 

  397. <li><p>The python packages that are specified in requirements.txt;</p></li>
    398. 

  398. </ul>
    399. 

  399. </details>
    400. 

  400. <details>
    401. 

  401. <summary>To train on nvidia GPUs</summary>
    402. 

  402. <ul class="simple">
    403. 

  403. <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>
    404. 

  404. <li><p>CuDNN &gt;= 8.1.x</p></li>
    405. 

  405. <li><p>Nvidia Driver with CUDA &gt;= 11.2 support (≥460.x)</p></li>
    406. 

  406. </ul>
    407. 

  407. </details>
    408. 

  408. </section>
    409. 

  409. <section id="quick-installation">
    410. 

  410. <h3>Quick Installation<a class="headerlink" href="#quick-installation" title="Permalink to this headline"></a></h3>
    411. 

  411. <details>
    412. 

  412. <summary>Install stable version using PyPi</summary>
    413. 

  413. <p>See in <a class="reference external" href="https://pypi.org/project/super-gradients/">PyPi</a></p>
    414. 

  414. <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install super-gradients
    415. 

  415. </pre></div>
    416. 

  416. </div>
    417. 

  417. <p>That’s it !</p>
    418. 

  418. </details>
    419. 

  419. <details>
    420. 

  420. <summary>Install using GitHub</summary>
    421. 

  421. <div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install git+https://github.com/Deci-AI/super-gradients.git@stable
    422. 

  422. </pre></div>
    423. 

  423. </div>
    424. 

  424. </details>
    425. 

  425. </section>
    426. 

  426. </section>
    427. 

  427. <section id="computer-vision-models-pretrained-checkpoints">
    428. 

  428. 

  429. 

  430. 

  431. <h2>Computer Vision Models - Pretrained Checkpoints<a class="headerlink" href="#computer-vision-models-pretrained-checkpoints" title="Permalink to this headline"></a></h2>
    432. 

  432. <section id="pretrained-classification-pytorch-checkpoints">
    433. 

  433. <h3>Pretrained Classification PyTorch Checkpoints<a class="headerlink" href="#pretrained-classification-pytorch-checkpoints" title="Permalink to this headline"></a></h3>
    434. 

  434. <table class="colwidths-auto docutils align-default">
    435. 

  435. <thead>
    436. 

  436. <tr class="row-odd"><th class="head"><p>Model</p></th>
    437. 

  437. <th class="head"><p>Dataset</p></th>
    438. 

  438. <th class="head"><p>Resolution</p></th>
    439. 

  439. <th class="head"><p>Top-1</p></th>
    440. 

  440. <th class="head"><p>Top-5</p></th>
    441. 

  441. <th class="head"><p>Latency (HW)*<sub>T4</sub></p></th>
    442. 

  442. <th class="head"><p>Latency (Production)**<sub>T4</sub></p></th>
    443. 

  443. <th class="head"><p>Latency (HW)*<sub>Jetson Xavier NX</sub></p></th>
    444. 

  444. <th class="head"><p>Latency (Production)**<sub>Jetson Xavier NX</sub></p></th>
    445. 

  445. <th class="text-center head"><p>Latency <sub>Cascade Lake</sub></p></th>
    446. 

  446. </tr>
    447. 

  447. </thead>
    448. 

  448. <tbody>
    449. 

  449. <tr class="row-even"><td><p>ViT base</p></td>
    450. 

  450. <td><p>ImageNet21K</p></td>
    451. 

  451. <td><p>224x224</p></td>
    452. 

  452. <td><p>84.15</p></td>
    453. 

  453. <td><p>-</p></td>
    454. 

  454. <td><p><strong>4.46ms</strong></p></td>
    455. 

  455. <td><p><strong>4.60ms</strong></p></td>
    456. 

  456. <td><p><strong>-</strong> *</p></td>
    457. 

  457. <td><p><strong>-</strong></p></td>
    458. 

  458. <td class="text-center"><p><strong>57.22ms</strong></p></td>
    459. 

  459. </tr>
    460. 

  460. <tr class="row-odd"><td><p>ViT large</p></td>
    461. 

  461. <td><p>ImageNet21K</p></td>
    462. 

  462. <td><p>224x224</p></td>
    463. 

  463. <td><p>85.64</p></td>
    464. 

  464. <td><p>-</p></td>
    465. 

  465. <td><p><strong>12.81ms</strong></p></td>
    466. 

  466. <td><p><strong>13.19ms</strong></p></td>
    467. 

  467. <td><p><strong>-</strong> *</p></td>
    468. 

  468. <td><p><strong>-</strong></p></td>
    469. 

  469. <td class="text-center"><p><strong>187.22ms</strong></p></td>
    470. 

  470. </tr>
    471. 

  471. <tr class="row-even"><td><p>BEiT</p></td>
    472. 

  472. <td><p>ImageNet21K</p></td>
    473. 

  473. <td><p>224x224</p></td>
    474. 

  474. <td><p>-</p></td>
    475. 

  475. <td><p>-</p></td>
    476. 

  476. <td><p><strong>-ms</strong></p></td>
    477. 

  477. <td><p><strong>-ms</strong></p></td>
    478. 

  478. <td><p><strong>-</strong> *</p></td>
    479. 

  479. <td><p><strong>-</strong></p></td>
    480. 

  480. <td class="text-center"><p><strong>-ms</strong></p></td>
    481. 

  481. </tr>
    482. 

  482. <tr class="row-odd"><td><p>EfficientNet B0</p></td>
    483. 

  483. <td><p>ImageNet</p></td>
    484. 

  484. <td><p>224x224</p></td>
    485. 

  485. <td><p>77.62</p></td>
    486. 

  486. <td><p>93.49</p></td>
    487. 

  487. <td><p><strong>0.93ms</strong></p></td>
    488. 

  488. <td><p><strong>1.38ms</strong></p></td>
    489. 

  489. <td><p><strong>-</strong> *</p></td>
    490. 

  490. <td><p><strong>-</strong></p></td>
    491. 

  491. <td class="text-center"><p><strong>3.44ms</strong></p></td>
    492. 

  492. </tr>
    493. 

  493. <tr class="row-even"><td><p>RegNet Y200</p></td>
    494. 

  494. <td><p>ImageNet</p></td>
    495. 

  495. <td><p>224x224</p></td>
    496. 

  496. <td><p>70.88</p></td>
    497. 

  497. <td><p>89.35</p></td>
    498. 

  498. <td><p><strong>0.63ms</strong></p></td>
    499. 

  499. <td><p><strong>1.08ms</strong></p></td>
    500. 

  500. <td><p><strong>2.16ms</strong></p></td>
    501. 

  501. <td><p><strong>2.47ms</strong></p></td>
    502. 

  502. <td class="text-center"><p><strong>2.06ms</strong></p></td>
    503. 

  503. </tr>
    504. 

  504. <tr class="row-odd"><td><p>RegNet Y400</p></td>
    505. 

  505. <td><p>ImageNet</p></td>
    506. 

  506. <td><p>224x224</p></td>
    507. 

  507. <td><p>74.74</p></td>
    508. 

  508. <td><p>91.46</p></td>
    509. 

  509. <td><p><strong>0.80ms</strong></p></td>
    510. 

  510. <td><p><strong>1.25ms</strong></p></td>
    511. 

  511. <td><p><strong>2.62ms</strong></p></td>
    512. 

  512. <td><p><strong>2.91ms</strong></p></td>
    513. 

  513. <td class="text-center"><p><strong>2.87ms</strong></p></td>
    514. 

  514. </tr>
    515. 

  515. <tr class="row-even"><td><p>RegNet Y600</p></td>
    516. 

  516. <td><p>ImageNet</p></td>
    517. 

  517. <td><p>224x224</p></td>
    518. 

  518. <td><p>76.18</p></td>
    519. 

  519. <td><p>92.34</p></td>
    520. 

  520. <td><p><strong>0.77ms</strong></p></td>
    521. 

  521. <td><p><strong>1.22ms</strong></p></td>
    522. 

  522. <td><p><strong>2.64ms</strong></p></td>
    523. 

  523. <td><p><strong>2.93ms</strong></p></td>
    524. 

  524. <td class="text-center"><p><strong>2.39ms</strong></p></td>
    525. 

  525. </tr>
    526. 

  526. <tr class="row-odd"><td><p>RegNet Y800</p></td>
    527. 

  527. <td><p>ImageNet</p></td>
    528. 

  528. <td><p>224x224</p></td>
    529. 

  529. <td><p>77.07</p></td>
    530. 

  530. <td><p>93.26</p></td>
    531. 

  531. <td><p><strong>0.74ms</strong></p></td>
    532. 

  532. <td><p><strong>1.19ms</strong></p></td>
    533. 

  533. <td><p><strong>2.77ms</strong></p></td>
    534. 

  534. <td><p><strong>3.04ms</strong></p></td>
    535. 

  535. <td class="text-center"><p><strong>2.81ms</strong></p></td>
    536. 

  536. </tr>
    537. 

  537. <tr class="row-even"><td><p>ResNet 18</p></td>
    538. 

  538. <td><p>ImageNet</p></td>
    539. 

  539. <td><p>224x224</p></td>
    540. 

  540. <td><p>70.6</p></td>
    541. 

  541. <td><p>89.64</p></td>
    542. 

  542. <td><p><strong>0.52ms</strong></p></td>
    543. 

  543. <td><p><strong>0.95ms</strong></p></td>
    544. 

  544. <td><p><strong>2.01ms</strong></p></td>
    545. 

  545. <td><p><strong>2.30ms</strong></p></td>
    546. 

  546. <td class="text-center"><p><strong>4.56ms</strong></p></td>
    547. 

  547. </tr>
    548. 

  548. <tr class="row-odd"><td><p>ResNet 34</p></td>
    549. 

  549. <td><p>ImageNet</p></td>
    550. 

  550. <td><p>224x224</p></td>
    551. 

  551. <td><p>74.13</p></td>
    552. 

  552. <td><p>91.7</p></td>
    553. 

  553. <td><p><strong>0.92ms</strong></p></td>
    554. 

  554. <td><p><strong>1.34ms</strong></p></td>
    555. 

  555. <td><p><strong>3.57ms</strong></p></td>
    556. 

  556. <td><p><strong>3.87ms</strong></p></td>
    557. 

  557. <td class="text-center"><p><strong>7.64ms</strong></p></td>
    558. 

  558. </tr>
    559. 

  559. <tr class="row-even"><td><p>ResNet 50</p></td>
    560. 

  560. <td><p>ImageNet</p></td>
    561. 

  561. <td><p>224x224</p></td>
    562. 

  562. <td><p>81.91</p></td>
    563. 

  563. <td><p>93.0</p></td>
    564. 

  564. <td><p><strong>1.03ms</strong></p></td>
    565. 

  565. <td><p><strong>1.44ms</strong></p></td>
    566. 

  566. <td><p><strong>4.78ms</strong></p></td>
    567. 

  567. <td><p><strong>5.10ms</strong></p></td>
    568. 

  568. <td class="text-center"><p><strong>9.25ms</strong></p></td>
    569. 

  569. </tr>
    570. 

  570. <tr class="row-odd"><td><p>MobileNet V3_large-150 epochs</p></td>
    571. 

  571. <td><p>ImageNet</p></td>
    572. 

  572. <td><p>224x224</p></td>
    573. 

  573. <td><p>73.79</p></td>
    574. 

  574. <td><p>91.54</p></td>
    575. 

  575. <td><p><strong>0.67ms</strong></p></td>
    576. 

  576. <td><p><strong>1.11ms</strong></p></td>
    577. 

  577. <td><p><strong>2.42ms</strong></p></td>
    578. 

  578. <td><p><strong>2.71ms</strong></p></td>
    579. 

  579. <td class="text-center"><p><strong>1.76ms</strong></p></td>
    580. 

  580. </tr>
    581. 

  581. <tr class="row-even"><td><p>MobileNet V3_large-300 epochs</p></td>
    582. 

  582. <td><p>ImageNet</p></td>
    583. 

  583. <td><p>224x224</p></td>
    584. 

  584. <td><p>74.52</p></td>
    585. 

  585. <td><p>91.92</p></td>
    586. 

  586. <td><p><strong>0.67ms</strong></p></td>
    587. 

  587. <td><p><strong>1.11ms</strong></p></td>
    588. 

  588. <td><p><strong>2.42ms</strong></p></td>
    589. 

  589. <td><p><strong>2.71ms</strong></p></td>
    590. 

  590. <td class="text-center"><p><strong>1.76ms</strong></p></td>
    591. 

  591. </tr>
    592. 

  592. <tr class="row-odd"><td><p>MobileNet V3_small</p></td>
    593. 

  593. <td><p>ImageNet</p></td>
    594. 

  594. <td><p>224x224</p></td>
    595. 

  595. <td><p>67.45</p></td>
    596. 

  596. <td><p>87.47</p></td>
    597. 

  597. <td><p><strong>0.55ms</strong></p></td>
    598. 

  598. <td><p><strong>0.96ms</strong></p></td>
    599. 

  599. <td><p><strong>2.01ms</strong> *</p></td>
    600. 

  600. <td><p><strong>2.35ms</strong></p></td>
    601. 

  601. <td class="text-center"><p><strong>1.06ms</strong></p></td>
    602. 

  602. </tr>
    603. 

  603. <tr class="row-even"><td><p>MobileNet V2_w1</p></td>
    604. 

  604. <td><p>ImageNet</p></td>
    605. 

  605. <td><p>224x224</p></td>
    606. 

  606. <td><p>73.08</p></td>
    607. 

  607. <td><p>91.1</p></td>
    608. 

  608. <td><p><strong>0.46 ms</strong></p></td>
    609. 

  609. <td><p><strong>0.89ms</strong></p></td>
    610. 

  610. <td><p><strong>1.65ms</strong> *</p></td>
    611. 

  611. <td><p><strong>1.90ms</strong></p></td>
    612. 

  612. <td class="text-center"><p><strong>1.56ms</strong></p></td>
    613. 

  613. </tr>
    614. 

  614. </tbody>
    615. 

  615. </table>
    616. 

  616. <blockquote>
    617. 

  617. <div><p><strong>NOTE:</strong> <br/></p>
    618. 

  618. <ul class="simple">
    619. 

  619. <li><p>Latency (HW)* - Hardware performance (not including IO)<br/></p></li>
    620. 

  620. <li><p>Latency (Production)** - Production Performance (including IO)</p></li>
    621. 

  621. <li><p>Performance measured for T4 and Jetson Xavier NX with TensorRT, using FP16 precision and batch size 1</p></li>
    622. 

  622. <li><p>Performance measured for Cascade Lake CPU with OpenVINO, using FP16 precision and batch size 1</p></li>
    623. 

  623. </ul>
    624. 

  624. </div></blockquote>
    625. 

  625. </section>
    626. 

  626. <section id="pretrained-object-detection-pytorch-checkpoints">
    627. 

  627. <h3>Pretrained Object Detection PyTorch Checkpoints<a class="headerlink" href="#pretrained-object-detection-pytorch-checkpoints" title="Permalink to this headline"></a></h3>
    628. 

  628. <table class="colwidths-auto docutils align-default">
    629. 

  629. <thead>
    630. 

  630. <tr class="row-odd"><th class="head"><p>Model</p></th>
    631. 

  631. <th class="head"><p>Dataset</p></th>
    632. 

  632. <th class="head"><p>Resolution</p></th>
    633. 

  633. <th class="head"><p>mAP<sup>val<br>0.5:0.95</p></th>
    634. 

  634. <th class="head"><p>Latency (HW)*<sub>T4</sub></p></th>
    635. 

  635. <th class="head"><p>Latency (Production)**<sub>T4</sub></p></th>
    636. 

  636. <th class="head"><p>Latency (HW)*<sub>Jetson Xavier NX</sub></p></th>
    637. 

  637. <th class="head"><p>Latency (Production)**<sub>Jetson Xavier NX</sub></p></th>
    638. 

  638. <th class="text-center head"><p>Latency <sub>Cascade Lake</sub></p></th>
    639. 

  639. </tr>
    640. 

  640. </thead>
    641. 

  641. <tbody>
    642. 

  642. <tr class="row-even"><td><p>SSD lite MobileNet v2</p></td>
    643. 

  643. <td><p>COCO</p></td>
    644. 

  644. <td><p>320x320</p></td>
    645. 

  645. <td><p>21.5</p></td>
    646. 

  646. <td><p><strong>0.77ms</strong></p></td>
    647. 

  647. <td><p><strong>1.40ms</strong></p></td>
    648. 

  648. <td><p><strong>5.28ms</strong></p></td>
    649. 

  649. <td><p><strong>6.44ms</strong></p></td>
    650. 

  650. <td class="text-center"><p><strong>4.13ms</strong></p></td>
    651. 

  651. </tr>
    652. 

  652. <tr class="row-odd"><td><p>SSD lite MobileNet v1</p></td>
    653. 

  653. <td><p>COCO</p></td>
    654. 

  654. <td><p>320x320</p></td>
    655. 

  655. <td><p>24.3</p></td>
    656. 

  656. <td><p><strong>1.55ms</strong></p></td>
    657. 

  657. <td><p><strong>2.84ms</strong></p></td>
    658. 

  658. <td><p><strong>8.07ms</strong></p></td>
    659. 

  659. <td><p><strong>9.14ms</strong></p></td>
    660. 

  660. <td class="text-center"><p><strong>22.76ms</strong></p></td>
    661. 

  661. </tr>
    662. 

  662. <tr class="row-even"><td><p>YOLOX nano</p></td>
    663. 

  663. <td><p>COCO</p></td>
    664. 

  664. <td><p>640x640</p></td>
    665. 

  665. <td><p>26.77</p></td>
    666. 

  666. <td><p><strong>2.47ms</strong></p></td>
    667. 

  667. <td><p><strong>4.09ms</strong></p></td>
    668. 

  668. <td><p><strong>11.49ms</strong></p></td>
    669. 

  669. <td><p><strong>12.97ms</strong></p></td>
    670. 

  670. <td class="text-center"><p><strong>-</strong></p></td>
    671. 

  671. </tr>
    672. 

  672. <tr class="row-odd"><td><p>YOLOX tiny</p></td>
    673. 

  673. <td><p>COCO</p></td>
    674. 

  674. <td><p>640x640</p></td>
    675. 

  675. <td><p>37.18</p></td>
    676. 

  676. <td><p><strong>3.16ms</strong></p></td>
    677. 

  677. <td><p><strong>4.61ms</strong></p></td>
    678. 

  678. <td><p><strong>15.23ms</strong></p></td>
    679. 

  679. <td><p><strong>19.24ms</strong></p></td>
    680. 

  680. <td class="text-center"><p><strong>-</strong></p></td>
    681. 

  681. </tr>
    682. 

  682. <tr class="row-even"><td><p>YOLOX small</p></td>
    683. 

  683. <td><p>COCO</p></td>
    684. 

  684. <td><p>640x640</p></td>
    685. 

  685. <td><p>40.47</p></td>
    686. 

  686. <td><p><strong>3.58ms</strong></p></td>
    687. 

  687. <td><p><strong>4.94ms</strong></p></td>
    688. 

  688. <td><p><strong>18.88ms</strong></p></td>
    689. 

  689. <td><p><strong>22.48ms</strong></p></td>
    690. 

  690. <td class="text-center"><p><strong>-</strong></p></td>
    691. 

  691. </tr>
    692. 

  692. <tr class="row-odd"><td><p>YOLOX medium</p></td>
    693. 

  693. <td><p>COCO</p></td>
    694. 

  694. <td><p>640x640</p></td>
    695. 

  695. <td><p>46.4</p></td>
    696. 

  696. <td><p><strong>6.40ms</strong></p></td>
    697. 

  697. <td><p><strong>7.65ms</strong></p></td>
    698. 

  698. <td><p><strong>39.22ms</strong></p></td>
    699. 

  699. <td><p><strong>44.5ms</strong></p></td>
    700. 

  700. <td class="text-center"><p><strong>-</strong></p></td>
    701. 

  701. </tr>
    702. 

  702. <tr class="row-even"><td><p>YOLOX large</p></td>
    703. 

  703. <td><p>COCO</p></td>
    704. 

  704. <td><p>640x640</p></td>
    705. 

  705. <td><p>49.25</p></td>
    706. 

  706. <td><p><strong>10.07ms</strong></p></td>
    707. 

  707. <td><p><strong>11.12ms</strong></p></td>
    708. 

  708. <td><p><strong>68.73ms</strong></p></td>
    709. 

  709. <td><p><strong>77.01ms</strong></p></td>
    710. 

  710. <td class="text-center"><p><strong>-</strong></p></td>
    711. 

  711. </tr>
    712. 

  712. </tbody>
    713. 

  713. </table>
    714. 

  714. <blockquote>
    715. 

  715. <div><p><strong>NOTE:</strong> <br/></p>
    716. 

  716. <ul class="simple">
    717. 

  717. <li><p>Latency (HW)* - Hardware performance (not including IO)<br/></p></li>
    718. 

  718. <li><p>Latency (Production)** - Production Performance (including IO)</p></li>
    719. 

  719. <li><p>Latency performance measured for T4 and Jetson Xavier NX with TensorRT, using FP16 precision and batch size 1</p></li>
    720. 

  720. <li><p>Latency performance measured for Cascade Lake CPU with OpenVINO, using FP16 precision and batch size 1</p></li>
    721. 

  721. </ul>
    722. 

  722. </div></blockquote>
    723. 

  723. </section>
    724. 

  724. <section id="pretrained-semantic-segmentation-pytorch-checkpoints">
    725. 

  725. <h3>Pretrained Semantic Segmentation PyTorch Checkpoints<a class="headerlink" href="#pretrained-semantic-segmentation-pytorch-checkpoints" title="Permalink to this headline"></a></h3>
    726. 

  726. <table class="colwidths-auto docutils align-default">
    727. 

  727. <thead>
    728. 

  728. <tr class="row-odd"><th class="head"><p>Model</p></th>
    729. 

  729. <th class="head"><p>Dataset</p></th>
    730. 

  730. <th class="head"><p>Resolution</p></th>
    731. 

  731. <th class="head"><p>mIoU</p></th>
    732. 

  732. <th class="head"><p>Latency b1<sub>T4</sub></p></th>
    733. 

  733. <th class="text-center head"><p>Latency b1<sub>T4</sub> including IO</p></th>
    734. 

  734. </tr>
    735. 

  735. </thead>
    736. 

  736. <tbody>
    737. 

  737. <tr class="row-even"><td><p>DDRNet 23</p></td>
    738. 

  738. <td><p>Cityscapes</p></td>
    739. 

  739. <td><p>1024x2048</p></td>
    740. 

  740. <td><p>80.26</p></td>
    741. 

  741. <td><p><strong>7.62ms</strong></p></td>
    742. 

  742. <td class="text-center"><p><strong>25.94ms</strong></p></td>
    743. 

  743. </tr>
    744. 

  744. <tr class="row-odd"><td><p>DDRNet 23 slim</p></td>
    745. 

  745. <td><p>Cityscapes</p></td>
    746. 

  746. <td><p>1024x2048</p></td>
    747. 

  747. <td><p>78.01</p></td>
    748. 

  748. <td><p><strong>3.56ms</strong></p></td>
    749. 

  749. <td class="text-center"><p><strong>22.80ms</strong></p></td>
    750. 

  750. </tr>
    751. 

  751. <tr class="row-even"><td><p>STDC 1-Seg50</p></td>
    752. 

  752. <td><p>Cityscapes</p></td>
    753. 

  753. <td><p>512x1024</p></td>
    754. 

  754. <td><p>75.07</p></td>
    755. 

  755. <td><p><strong>2.83ms</strong></p></td>
    756. 

  756. <td class="text-center"><p><strong>12.57ms</strong></p></td>
    757. 

  757. </tr>
    758. 

  758. <tr class="row-odd"><td><p>STDC 1-Seg75</p></td>
    759. 

  759. <td><p>Cityscapes</p></td>
    760. 

  760. <td><p>768x1536</p></td>
    761. 

  761. <td><p>77.8</p></td>
    762. 

  762. <td><p><strong>5.71ms</strong></p></td>
    763. 

  763. <td class="text-center"><p><strong>26.70ms</strong></p></td>
    764. 

  764. </tr>
    765. 

  765. <tr class="row-even"><td><p>STDC 2-Seg50</p></td>
    766. 

  766. <td><p>Cityscapes</p></td>
    767. 

  767. <td><p>512x1024</p></td>
    768. 

  768. <td><p>75.79</p></td>
    769. 

  769. <td><p><strong>3.74ms</strong></p></td>
    770. 

  770. <td class="text-center"><p><strong>13.89ms</strong></p></td>
    771. 

  771. </tr>
    772. 

  772. <tr class="row-odd"><td><p>STDC 2-Seg75</p></td>
    773. 

  773. <td><p>Cityscapes</p></td>
    774. 

  774. <td><p>768x1536</p></td>
    775. 

  775. <td><p>78.93</p></td>
    776. 

  776. <td><p><strong>7.35ms</strong></p></td>
    777. 

  777. <td class="text-center"><p><strong>28.18ms</strong></p></td>
    778. 

  778. </tr>
    779. 

  779. <tr class="row-even"><td><p>RegSeg (exp48)</p></td>
    780. 

  780. <td><p>Cityscapes</p></td>
    781. 

  781. <td><p>1024x2048</p></td>
    782. 

  782. <td><p>78.15</p></td>
    783. 

  783. <td><p><strong>13.09ms</strong></p></td>
    784. 

  784. <td class="text-center"><p><strong>41.88ms</strong></p></td>
    785. 

  785. </tr>
    786. 

  786. <tr class="row-odd"><td><p>Larger RegSeg (exp53)</p></td>
    787. 

  787. <td><p>Cityscapes</p></td>
    788. 

  788. <td><p>1024x2048</p></td>
    789. 

  789. <td><p>79.2</p></td>
    790. 

  790. <td><p><strong>24.82ms</strong></p></td>
    791. 

  791. <td class="text-center"><p><strong>51.87ms</strong></p></td>
    792. 

  792. </tr>
    793. 

  793. <tr class="row-even"><td><p>ShelfNet LW 34</p></td>
    794. 

  794. <td><p>COCO Segmentation (21 classes from PASCAL including background)</p></td>
    795. 

  795. <td><p>512x512</p></td>
    796. 

  796. <td><p>65.1</p></td>
    797. 

  797. <td><p><strong>-</strong></p></td>
    798. 

  798. <td class="text-center"><p><strong>-</strong></p></td>
    799. 

  799. </tr>
    800. 

  800. </tbody>
    801. 

  801. </table>
    802. 

  802. <blockquote>
    803. 

  803. <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>
    804. 

  804. </div></blockquote>
    805. 

  805. </section>
    806. 

  806. </section>
    807. 

  807. <section id="implemented-model-architectures">
    808. 

  808. <h2>Implemented Model Architectures<a class="headerlink" href="#implemented-model-architectures" title="Permalink to this headline"></a></h2>
    809. 

  809. <section id="image-classification">
    810. 

  810. <h3>Image Classification<a class="headerlink" href="#image-classification" title="Permalink to this headline"></a></h3>
    811. 

  811. <ul class="simple">
    812. 

  812. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/densenet.py">DensNet (Densely Connected Convolutional Networks)</a> - Densely Connected Convolutional Networks <a class="reference external" href="https://arxiv.org/pdf/1608.06993.pdf">https://arxiv.org/pdf/1608.06993.pdf</a></p></li>
    813. 

  813. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/dpn.py">DPN</a> - Dual Path Networks <a class="reference external" href="https://arxiv.org/pdf/1707.01629">https://arxiv.org/pdf/1707.01629</a></p></li>
    814. 

  814. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/efficientnet.py">EfficientNet</a> - <a class="reference external" href="https://arxiv.org/abs/1905.11946">https://arxiv.org/abs/1905.11946</a></p></li>
    815. 

  815. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/googlenet.py">GoogleNet</a> - <a class="reference external" href="https://arxiv.org/pdf/1409.4842">https://arxiv.org/pdf/1409.4842</a></p></li>
    816. 

  816. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/lenet.py">LeNet</a> - <a class="reference external" href="http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf">https://yann.lecun.com/exdb/lenet/</a></p></li>
    817. 

  817. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenet.py">MobileNet</a> - Efficient Convolutional Neural Networks for Mobile Vision Applications <a class="reference external" href="https://arxiv.org/pdf/1704.04861">https://arxiv.org/pdf/1704.04861</a></p></li>
    818. 

  818. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenetv2.py">MobileNet v2</a> - <a class="reference external" href="https://arxiv.org/pdf/1801.04381">https://arxiv.org/pdf/1801.04381</a></p></li>
    819. 

  819. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/mobilenetv3.py">MobileNet v3</a> - <a class="reference external" href="https://arxiv.org/pdf/1905.02244">https://arxiv.org/pdf/1905.02244</a></p></li>
    820. 

  820. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/pnasnet.py">PNASNet</a> - Progressive Neural Architecture Search Networks <a class="reference external" href="https://arxiv.org/pdf/1712.00559">https://arxiv.org/pdf/1712.00559</a></p></li>
    821. 

  821. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/preact_resnet.py">Pre-activation ResNet</a> - <a class="reference external" href="https://arxiv.org/pdf/1603.05027">https://arxiv.org/pdf/1603.05027</a></p></li>
    822. 

  822. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/regnet.py">RegNet</a> - <a class="reference external" href="https://arxiv.org/pdf/2003.13678.pdf">https://arxiv.org/pdf/2003.13678.pdf</a></p></li>
    823. 

  823. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/repvgg.py">RepVGG</a> - Making VGG-style ConvNets Great Again <a class="reference external" href="https://arxiv.org/pdf/2101.03697.pdf">https://arxiv.org/pdf/2101.03697.pdf</a></p></li>
    824. 

  824. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/resnet.py">ResNet</a> - Deep Residual Learning for Image Recognition <a class="reference external" href="https://arxiv.org/pdf/1512.03385">https://arxiv.org/pdf/1512.03385</a></p></li>
    825. 

  825. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/resnext.py">ResNeXt</a> - Aggregated Residual Transformations for Deep Neural Networks <a class="reference external" href="https://arxiv.org/pdf/1611.05431">https://arxiv.org/pdf/1611.05431</a></p></li>
    826. 

  826. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/senet.py">SENet </a> - Squeeze-and-Excitation Networks<a class="reference external" href="https://arxiv.org/pdf/1709.01507">https://arxiv.org/pdf/1709.01507</a></p></li>
    827. 

  827. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/shufflenet.py">ShuffleNet</a> - <a class="reference external" href="https://arxiv.org/pdf/1707.01083">https://arxiv.org/pdf/1707.01083</a></p></li>
    828. 

  828. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/shufflenetv2.py">ShuffleNet v2</a> - Efficient Convolutional Neural Network for Mobile
    829. 

  829. Devices<a class="reference external" href="https://arxiv.org/pdf/1807.11164">https://arxiv.org/pdf/1807.11164</a></p></li>
    830. 

  830. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/classification_models/vgg.py">VGG</a> - Very Deep Convolutional Networks for Large-scale Image Recognition <a class="reference external" href="https://arxiv.org/pdf/1409.1556">https://arxiv.org/pdf/1409.1556</a></p></li>
    831. 

  831. </ul>
    832. 

  832. </section>
    833. 

  833. <section id="object-detection">
    834. 

  834. <h3>Object Detection<a class="headerlink" href="#object-detection" title="Permalink to this headline"></a></h3>
    835. 

  835. <ul class="simple">
    836. 

  836. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/csp_darknet53.py">CSP DarkNet</a></p></li>
    837. 

  837. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/darknet53.py">DarkNet-53</a></p></li>
    838. 

  838. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/ssd.py">SSD (Single Shot Detector)</a> - <a class="reference external" href="https://arxiv.org/pdf/1512.02325">https://arxiv.org/pdf/1512.02325</a></p></li>
    839. 

  839. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/detection_models/yolox.py">YOLOX</a> - <a class="reference external" href="https://arxiv.org/abs/2107.08430">https://arxiv.org/abs/2107.08430</a></p></li>
    840. 

  840. </ul>
    841. 

  841. </section>
    842. 

  842. <section id="semantic-segmentation">
    843. 

  843. <h3>Semantic Segmentation<a class="headerlink" href="#semantic-segmentation" title="Permalink to this headline"></a></h3>
    844. 

  844. <ul class="simple">
    845. 

  845. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/ddrnet.py">DDRNet (Deep Dual-resolution Networks)</a> - <a class="reference external" href="https://arxiv.org/pdf/2101.06085.pdf">https://arxiv.org/pdf/2101.06085.pdf</a></p></li>
    846. 

  846. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/laddernet.py">LadderNet</a> - Multi-path networks based on U-Net for medical image segmentation <a class="reference external" href="https://arxiv.org/pdf/1810.07810">https://arxiv.org/pdf/1810.07810</a></p></li>
    847. 

  847. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/regseg.py">RegSeg</a> - Rethink Dilated Convolution for Real-time Semantic Segmentation <a class="reference external" href="https://arxiv.org/pdf/2111.09957">https://arxiv.org/pdf/2111.09957</a></p></li>
    848. 

  848. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/shelfnet.py">ShelfNet</a> - <a class="reference external" href="https://arxiv.org/pdf/1811.11254">https://arxiv.org/pdf/1811.11254</a></p></li>
    849. 

  849. <li><p><a class="reference external" href="https://github.com/Deci-AI/super-gradients/blob/master/src/super_gradients/training/models/segmentation_models/stdc.py">STDC</a> - Rethinking BiSeNet For Real-time Semantic Segmentation <a class="reference external" href="https://arxiv.org/pdf/2104.13188">https://arxiv.org/pdf/2104.13188</a></p></li>
    850. 

  850. </ul>
    851. 

  851. </details>
    852. 

  852. </section>
    853. 

  853. </section>
    854. 

  854. 

  855. 

  856. 

  857. 

  858. 

  859. 

  860. 

  861. 

  862. 

  863. 

  864. 

  865. 

  866. 

  867. 

  868. 

  869. 

  870. 

  871. 

  872. 

  873. 

  874. 

  875. 

  876. 

  877. 

  878. 

  879. 

  880. 

  881. 

  882. 

  883. 

  884. 

  885. <section id="contributing">
    886. 

  886. <h2>Contributing<a class="headerlink" href="#contributing" title="Permalink to this headline"></a></h2>
    887. 

  887. <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>
    888. 

  888. <p>Our awesome contributors:</p>
    889. 

  889. <a href="https://github.com/Deci-AI/super-gradients/graphs/contributors">
    890. 

  890. <img src="https://contrib.rocks/image?repo=Deci-AI/super-gradients" />
    891. 

  891. </a>
    892. 

  892. <p><br/>Made with <a class="reference external" href="https://contrib.rocks">contrib.rocks</a>.</p>
    893. 

  893. </section>
    894. 

  894. <section id="citation">
    895. 

  895. <h2>Citation<a class="headerlink" href="#citation" title="Permalink to this headline"></a></h2>
    896. 

  896. <p>If you are using SuperGradients library or benchmarks in your research, please cite SuperGradients deep learning training library.</p>
    897. 

  897. </section>
    898. 

  898. <section id="community">
    899. 

  899. <h2>Community<a class="headerlink" href="#community" title="Permalink to this headline"></a></h2>
    900. 

  900. <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,
    901. 

  901. or want to file a bug or issue report, we would love to welcome you aboard!</p>
    902. 

  902. <ul class="simple">
    903. 

  903. <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>
    904. 

  904. <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>
    905. 

  905. <li><p>You can also join the <a class="reference external" href="https://deci.ai/resources/blog/">community mailing list</a>
    906. 

  906. to ask questions about the project and receive announcements.</p></li>
    907. 

  907. <li><p>For a short meeting with SuperGradients PM, use this <a class="reference external" href="https://calendly.com/ofer-baratz-deci/15min">link</a> and choose your preferred time.</p></li>
    908. 

  908. </ul>
    909. 

  909. </section>
    910. 

  910. <section id="license">
    911. 

  911. <h2>License<a class="headerlink" href="#license" title="Permalink to this headline"></a></h2>
    912. 

  912. <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>
    913. 

  913. </section>
    914. 

  914. <hr class="docutils" />
    915. 

  915. <section id="deci-lab">
    916. 

  916. <h2>Deci Lab<a class="headerlink" href="#deci-lab" title="Permalink to this headline"></a></h2>
    917. 

  917. <p>Deci Lab supports all common frameworks and Hardware, from Intel CPUs to Nvidia’s GPUs and Jetsons</p>
    918. 

  918. <p>You can enjoy immediate improvement in throughput, latency, and memory with the Deci Lab. It optimizes deep learning models using best-of-breed technologies, such as quantization and graph compilers.</p>
    919. 

  919. <p>Get a complete benchmark of your models’ performance on different hardware and batch sizes in a single interface. Invite co-workers to collaborate on models and communicate your progress.</p>
    920. 

  920. <p>Sign up for Deci Lab for free <a class="reference external" href="https://console.deci.ai/">here</a></p>
    921. 

  921. </section>
    922. 

  922. </section>
    923. 

  923. 

  924. 

  925.        </div>
    926. 

  926.       </div>
    927. 

  927.       <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
    928. 

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  932. <hr/>
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  934. <div role="contentinfo">
    935. 

  935. <p>&#169; Copyright 2021, SuperGradients team.</p>
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