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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 NX (16GB) Jetson Orin Nano (8GB)

  2. Running one of the following versions of JetPack :

    JetPack 5 (L4T r35.x) JetPack 6 (L4T r36.x)

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

    • 7.2 GB for container image
    • Spaces for models

  4. Clone and setup jetson-containers : 

    git clone https://github.com/dusty-nv/jetson-containers
bash jetson-containers/install.sh

How to start

Use the jetson-containers run and autotag commands to automatically pull or build a compatible container image. 

jetson-containers run --workdir /opt/nanoowl $(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 tree_demo.py ../../data/owl_image_encoder_patch32.engine

    Info

    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 \
    data/owl_image_encoder_patch32.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)

Result