This demo showcases the capabilities of ExecuTorch's JavaScript bindings. It is able to load a model, run inference, and classify an image natively in the browser.

Emscripten is necessary to compile ExecuTorch for Wasm. You can install Emscripten with these commands:

# Clone the emsdk repository
git clone https://github.com/emscripten-core/emsdk.git
cd emsdk

# Download and install version 4.0.10 of the SDK
./emsdk install 4.0.10
./emsdk activate 4.0.10

# Add the Emscripten environment variables to your shell
source ./emsdk_env.sh

Make sure you have the system requirements listed in the Getting Started Guide before continuing.

1. Install ExecuTorch from PyPI.

pip3 install executorch

2. Update the ExecuTorch submodule.

git submodule update --init --recursive executorch

3. Generate the EfficientSAM binary file for this demo.

bash export.sh

It should output a file called xnnpack_efficient_sam.pte.

Once you have Emscripten installed, ExecuTorch set up, and the model file generated, you can build and run the demo. Building may take up to 9 minutes.

cd efficient_sam/wasm # The directory containing this README

# Build the demo
bash build.sh

# Run the demo
python3 -m http.server --directory build/

The page will be available at http://localhost:8000/demo.html.

• Load a model from a file
  • This demo only supports the EfficientSAM model. Passing in a model with different input/output shapes will result in an error.
• Run inference on an image
  • Supported formats: .png, .gif, .jpeg, .jpg
• Select a point on the image to run inference
  • May take around 6.5 seconds to run inference
• Show and hide the segmentation mask