Documentation
The purpose of SvelteML is to offer simple Components that can make ML more accessible. It leverages TensorflowJS to offer Svelte apps ML features out-of-the-box. It relies heavily on Svelte's reactivity feature and event hooks can be used to extend out the ML flow. e.g. on:poses in the Pose Estimator will give you the raw poses directfrom TensorflowJS.
npm install svelteml --save
All Components try to be reactive so although it feels very declarative, it is also reacting to your input. Add an issue in Github if you need a specific behaviour or if there is a bug or would like to recommend something. You know the drill.
- Image Classification
- Body Segmentation
- Basic Multi-Pose Estimation
- Object Detection
- Face Mesh
- Hand Pose Detection
- Sentence Encoding
- Text Toxicity
- Blur Body Parts
- Bokeh Effect
- Speech Command Recognition