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
- Image Classification
- Body Segmentation
- Basic Multi-Pose Estimation
- Object Detection
- Sentence Encoding
- Text Toxicity
- Question and Answers
- Blur Body Parts
- Bokeh Effect
- Face Mesh
- Hand Pose Detection
- Switching to Lerna for multiple repos so the lib can expand in the different areas. Also helpful for tfjs3 when it will have code-splitting 😃
- @svelteml/ui
- @svelteml/classification
- @svelteml/segmentation
- @svelteml/automl
- @svelteml/text
- @svelteml/audio
- Unlock slots with Facial recognition, maybe use faceapi.js
- Demo site for more details
- Audio and speech recognition features
- Additional models using the lower level tfjs apis
- .... and a few other top secret ideas 🤭
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.