Instructions to use onnx-community/maskformer-resnet50-ade20k-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use onnx-community/maskformer-resnet50-ade20k-full with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'onnx-community/maskformer-resnet50-ade20k-full');
| library_name: transformers.js | |
| pipeline_tag: image-segmentation | |
| https://huggingface.co/facebook/maskformer-resnet50-ade20k-full with ONNX weights to be compatible with Transformers.js. | |
| Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |