Apple's SHARP running in the browser via ONNX runtime web
RepositoryFreeHi HN, author here. SHARP is Apple's recent single-image 3D Gaussian splatting model (https://arxiv.org/abs/2512.10685). Their reference code is PyTorch + a pretty heavy pipeline; I wanted to see if it could run in a browser with no server hop, so I exported the predictor to
- Best for
- browser-based model inference, interactive model visualization, model performance benchmarking
- Type
- Repository · Free
- Score
- 42/100
- Best alternative
- Stable Diffusion
Capabilities3 decomposed
browser-based model inference
Medium confidenceThis capability allows users to run Apple's SHARP model directly in the browser using the ONNX Runtime Web, which leverages WebAssembly for efficient execution. The model is optimized for performance in a web environment, enabling real-time inference without the need for server-side processing. This approach minimizes latency and enhances user experience by processing data locally within the browser.
Utilizes ONNX Runtime Web's WebAssembly execution for optimized performance in a browser, unlike traditional server-side ML solutions.
More efficient than server-based inference solutions as it eliminates round-trip latency by processing data directly in the browser.
interactive model visualization
Medium confidenceThis capability provides users with an interactive interface to visualize the outputs of the SHARP model in real-time. It employs JavaScript libraries for dynamic rendering and allows users to manipulate input data directly, observing how changes affect model predictions. This feature enhances understanding and engagement with the model's behavior.
Integrates real-time data manipulation with immediate feedback, enhancing user interactivity compared to static visualizations.
Offers a more engaging experience than traditional static visualizations by allowing users to see the effects of their inputs instantly.
model performance benchmarking
Medium confidenceThis capability enables users to benchmark the performance of the SHARP model in the browser against various datasets. It collects metrics such as inference time and accuracy, providing insights into the model's efficiency and effectiveness. The benchmarking process is automated and can be easily integrated into development workflows.
Automates the benchmarking process within the browser environment, allowing for quick iterations and immediate feedback.
More accessible than traditional benchmarking tools that require server-side infrastructure, making it easier for developers to test in real-time.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓web developers looking to integrate ML capabilities into client-side applications
- ✓data scientists and developers who need to explain model behavior to stakeholders
- ✓developers and researchers looking to optimize ML models for web deployment
Known Limitations
- ⚠Performance may vary based on browser capabilities and hardware; large models may lead to increased load times.
- ⚠Limited to visualizations supported by the underlying libraries; complex models may not be fully represented.
- ⚠Benchmarking results can be influenced by the user's hardware and browser; may not reflect server-side performance.
Requirements
Input / Output
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