mirror of https://github.com/tracel-ai/burn.git
78 lines
2.7 KiB
Markdown
78 lines
2.7 KiB
Markdown
# Image Classification Web Demo Using Burn and WebAssembly
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[](https://antimora.github.io/image-classification/)
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## Overview
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This demo showcases how to execute an image classification task in a web browser using a model
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converted to Rust code. The project utilizes the Burn deep learning framework, WebGPU and
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WebAssembly . Specifically, it demonstrates:
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1. Converting an ONNX (Open Neural Networks Exchange) model into Rust code compatible with the Burn
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framework.
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2. Executing the model within a web browser using WebGPU via the `burn-wgpu` backend and WebAssembly
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through the `burn-ndarray` and `burn-candle` backends.
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## Running the Demo
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### Step 1: Build the WebAssembly Binary and Other Assets
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To compile the Rust code into WebAssembly and build other essential files, execute the following
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script:
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```bash
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./build-for-web.sh
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```
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### Step 2: Launch the Web Server
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Run the following command to initiate a web server on your local machine:
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```bash
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./run-server.sh
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```
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### Step 3: Access the Web Demo
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Open your web browser and navigate to:
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```plaintext
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http://localhost:8000
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```
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## Backend Compatibility
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As of now, the WebGPU backend is compatible only with Chrome browsers running on macOS and Windows.
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The application will dynamically detect if WebGPU support is available and proceed accordingly.
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## SIMD Support
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The build targets two sets of binaries, one with SIMD support and one without. The web application
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dynamically detects if SIMD support is available and downloads the appropriate binary.
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## Model Information
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The image classification task is achieved using the SqueezeNet model, a compact Convolutional Neural
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Network (CNN). It is trained on the ImageNet dataset and can classify images into 1,000 distinct
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categories. The included ONNX model is sourced from the
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[ONNX Model Zoo](https://github.com/onnx/models/tree/main/vision/classification/squeezenet). For
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further details about the model's architecture and performance, you can refer to the
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[original paper](https://arxiv.org/abs/1602.07360).
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## Credits
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This demo was inspired by the ONNX Runtime web demo featuring the
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[SqueezeNet model trained on ImageNet](https://microsoft.github.io/onnxruntime-web-demo/#/squeezenet).
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The complete list of credits/attribution can be found in the [NOTICES](NOTICES.md) file.
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## Future Enhancements
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- [ ] Fall back to WebGL if WebGPU is not supported by the browser. See
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[wgpu's WebGL support ](https://github.com/gfx-rs/wgpu/wiki/Running-on-the-Web-with-WebGPU-and-WebGL)
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- [ ] Enable SIMD support for Safari browsers after Release 179.
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- [ ] Add image paste functionality to allow users to paste an image from the clipboard.
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