Skip to content

Tutorial - NanoSAM

Let's run NVIDIA's NanoSAM to check out the performance gain by distillation.

What you need

  1. One of the following Jetson:

    Jetson AGX Orin 64GB Jetson AGX Orin (32GB) Jetson Orin Nano Orin (8GB)

  2. Running one of the following JetPack.5x

    JetPack 5.1.2 (L4T r35.4.1) JetPack 5.1.1 (L4T r35.3.1) JetPack 5.1 (L4T r35.2.1)

  3. Sufficient storage space (preferably with NVMe SSD).

    • 6.3GB for container image
    • Spaces for models

Set up a container for nanosam

Clone jetson-containers

See jetson-containers' nanosam package README for more infomation**

git clone
cd jetson-containers
sudo apt update; sudo apt install -y python3-pip
pip3 install -r requirements.txt

How to start

Use and autotag script to automatically pull or build a compatible container image.

cd jetson-containers
./ $(./autotag nanosam)

Run examples

Inside the container, you can move to /opt/nanosam directory, to go through all the examples demonstrated on the repo.

cd /opt/nanosam

To run the "Example 1 - Segment with bounding box":

python3 examples/ \
    --image_encoder="data/resnet18_image_encoder.engine" \

The result is saved under /opt/nanosam/data/basic_usage_out.jpg.

To check on your host machine, you can copy that into /data directory of the container where that is mounted from the host.

cp data/basic_usage_out.jpg /data/

Then you can go to your host system, and find the file under the jetson_containers' data directory, like jetson_containers/data/basic_usage_out.jpg.