Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A distilled Segment Anything (SAM) model capable of running real-time with NVIDIA TensorRT
| Date | Stars |
|---|---|
| 2026-07-30 | 900 |
| 2026-08-08 | 904 |
| 2026-08-16 | 905 |
| 2026-08-18 | 905 |
| 2026-08-19 | 905 |
| 2026-08-24 | 906 |
| 2026-08-26 | 907 |
| 2026-08-27 | 909 |
| 2026-08-28 | 910 |
| 2026-09-02 | 911 |
| 2026-09-03 | 912 |
| 2026-09-06 | 913 |
| 2026-09-08 | 914 |
| 2026-09-09 | 915 |
| 2026-09-11 | 917 |
| 2026-09-15 | 918 |
| 2026-09-23 | 918 |
| 2026-09-28 | 919 |
| 2026-10-13 | 920 |
| 2026-10-16 | 921 |
| 2026-10-18 | 920 |
| 2026-10-20 | 922 |
| 2026-10-29 | 923 |
| 2026-11-04 | 923 |
Today
— stars today
This week
+1 stars this week
This month
+4 stars this month
Momentum
0.0
growth rate 0.11%/day
<h1 align="center"><span>NanoSAM</span></h1>
<p align="center"><a href="#usage"/>👍 Usage</a> - <a href="#performance"/>⏱️ Performance</a> - <a href="#setup">🛠️ Setup</a> - <a href="#examples">🤸 Examples</a> - <a href="#training">🏋️ Training</a> <br>- <a href="#evaluation">🧐 Evaluation</a> - <a href="#acknowledgement">👏 Acknowledgment</a> - <a href="#see-also">🔗 See also</a></p>
NanoSAM is a [Segment Anything (SAM)](https://github.com/facebookresearch/segment-anything) model variant that is capable of running in 🔥 ***real-time*** 🔥 on [NVIDIA Jetson Orin Platforms](https://store.nvidia.com/en-us/jetson/store) with [NVIDIA TensorRT](https://developer.nvidia.com/tensorrt).
<!-- <img src="assets/tshirt_gif_compressed_v2.gif" height="20%" width="20%"/> -->
<p align="center"><img src="assets/basic_usage_out.jpg" height="256px"/></p>
<!--<img src="assets/mouse_gif_compressed.gif" height="50%" width="50%"/> -->
> NanoSAM is trained by distilling the [MobileSAM](https://github.com/ChaoningZhang/MobileSAM) image encoder
> on unlabeled images. For an introduction to knowledge distillation, we recommend checking out [this tutorial](https://github.com/NVIDIA-AI-IOT/jetson-intro-to-distillation).
<a id="usage"></a>
## 👍 Usage
Using NanoSAM from Python looks like this
```python3
from nanosam.utils.predictor import Predictor
predictor = Predictor(
image_encoder="data/resnet18_image_encoder.engine",
mask_decoder="data/mobile_sam_mask_decoder.engine"
)
image = PIL.Image.open("dog.jpg")
predictor.set_image(image)
mask, _, _ = predictor.predict(np.array([[x, y]]), np.array([1]))
```
<details>
<summary>Notes</summary>
The point labels may be
| Point Label | Description |
|:--------------------:|-------------|
| 0 | Background point |
| 1 | Foreground point |
| 2 | Bounding box top-left |
| 3 | Bounding box bottom-right |
</details>
> Follow the instructions below for how to build the engine files.
<a id="performance"></a>
## ⏱️ Performance
NanoSAM runs real-time on Jetson Orin Nano.
<table style="border-top: solid 1px; border-left: solid 1px; border-right: solid 1px; border-bottom: solid 1px">
<thead>
<tr>
<th rowspan=2 style="text-align: center; border-right: solid 1px">Model †</th>
<th colspan=2 style="text-align: center; border-right: solid 1px">:stopwatch: Jetson Orin Nano (ms)</th>
<th colspan=2 style="text-align: center; border-right: solid 1px">:stopwatch: Jetson AGX Orin (ms)</th>
<th colspan=4 style="text-align: center; border-right: solid 1px"> :dart: Accuracy (mIoU) ‡</th>
</tr>
<tr>
<th style="text-align: center; border-right: solid 1px">Image Encoder</th>
<th style="text-align: center; border-right: solid 1px">Full Pipeline</th>
<th style="text-align: center; border-right: solid 1px">Image Encoder</th>
<th style="text-align: center; border-right: solid 1px">Full Pipeline</th>
<th style="text-align: center; border-right: solid 1px">All</th>
<th style="text-align: center; border-right: solid 1px">Small</th>
<th style="text-align: center; border-right: solid 1px">Medium</th>
<th style="text-align: center; border-right: solid 1px">Large</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: center; border-right: solid 1px">MobileSAM</td>
<td style="text-align: center; border-right: solid 1px">TBD</td>
<td style="text-align: center; border-right: solid 1px">146</td>
<td style="text-align: center; border-right: solid 1px">35</td>
<td style="text-align: center; border-right: solid 1px">39</td>
<td style="text-align: center; border-right: solid 1px">0.728</td>
<td style="text-align: center; border-right: solid 1px">0.658</td>
<td style="text-align: center; border-right: solid 1px">0.759</td>
<td style="Excerpt of 15,775 characters
Read on GitHub
John · NVIDIA
145
121
22
8
3
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ef49c4e73b13957d, topic:tensorrt