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2 | 2 | layout: blog_detail
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3 | 3 | title: 'Running PyTorch Models on Jetson Nano'
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4 | 4 | author: Jeff Tang, Hamid Shojanazeri, Geeta Chauhan
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5 |
| -featured-img: '' |
| 5 | +featured-img: 'assets/images/pytorch-logo.jpg' |
6 | 6 | ---
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7 | 7 |
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8 | 8 | ### Overview
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@@ -231,19 +231,19 @@ Using the same test files used in the PyTorch iOS YOLOv5 demo app or Android YOL
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231 | 231 | <img src="{{ site.baseurl }}/assets/images/blog-2022-3-10-using-pytorch-1.png" alt="PyTorch YOLOv5 on Jetson Nano, example with a dog" width="35%">
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232 | 232 | <img src="{{ site.baseurl }}/assets/images/blog-2022-3-10-using-pytorch-2.jpeg" alt="PyTorch YOLOv5 on Jetson Nano, example with a horse and a rider" width="50%">
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233 | 233 | </div>
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234 |
| -**Figure 1**. *PyTorch YOLOv5 on Jetson Nano*. |
| 234 | +Figure 1. PyTorch YOLOv5 on Jetson Nano. |
235 | 235 |
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236 | 236 | <div style="display: flex">
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237 | 237 | <img src="{{ site.baseurl }}/assets/images/blog-2022-3-10-using-pytorch-3.png" alt="PyTorch YOLOv5 on iOS, example with a dog" width="35%">
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238 | 238 | <img src="{{ site.baseurl }}/assets/images/blog-2022-3-10-using-pytorch-4.png" alt="PyTorch YOLOv5 on iOS, example with a horse and a rider" width="50%">
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239 | 239 | </div>
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240 |
| -**Figure 2**. *PyTorch YOLOv5 on iOS*. |
| 240 | +Figure 2. PyTorch YOLOv5 on iOS. |
241 | 241 |
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242 | 242 | <div style="display: flex">
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243 | 243 | <img src="{{ site.baseurl }}/assets/images/blog-2022-3-10-using-pytorch-5.png" alt="PyTorch YOLOv5 on Android, example with a dog" width="35%">
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244 | 244 | <img src="{{ site.baseurl }}/assets/images/blog-2022-3-10-using-pytorch-6.png" alt="PyTorch YOLOv5 on Android, example with a horse and a rider" width="50%">
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245 | 245 | </div>
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246 |
| -**Figure 2**. *PyTorch YOLOv5 on Android*. |
| 246 | +Figure 3. PyTorch YOLOv5 on Android. |
247 | 247 |
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248 | 248 | ### Summary
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249 | 249 | Based on our experience of running different PyTorch models for potential demo apps on Jetson Nano, we see that even Jetson Nano, a lower-end of the Jetson family of products, provides a powerful GPU and embedded system that can directly run some of the latest PyTorch models, pre-trained or transfer learned, efficiently.
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