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Tutorial - NanoOWL

Let's run NanoOWL, OWL-ViT optimized to run real-time on Jetson with NVIDIA TensorRT.

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).

    • 7.2 GB for container image
    • Spaces for models

Clone and set up jetson-containers

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 nanoowl)

How to run the tree prediction (live camera) example

  1. Ensure you have a camera device connected

    ls /dev/video*

    If no video device is found, exit from the container and check if you can see a video device on the host side.

  2. Launch the demo

    cd examples/tree_demo
    python3 ../../data/owl_image_encoder_patch32.engine


    If it fails to find or load the TensorRT engine file, build the TensorRT engine for the OWL-ViT vision encoder on your Jetson device.

    python3 -m nanoowl.build_image_encoder_engine \
  3. Second, open your browser to http://<ip address>:7860

  4. Type whatever prompt you like to see what works!

    Here are some examples

    • Example: [a face [a nose, an eye, a mouth]]
    • Example: [a face (interested, yawning / bored)]
    • Example: (indoors, outdoors)