Instructions to use HanzhiZhang/lowResSegModel_Object with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HanzhiZhang/lowResSegModel_Object with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="HanzhiZhang/lowResSegModel_Object")# Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("HanzhiZhang/lowResSegModel_Object") model = AutoModelForMaskGeneration.from_pretrained("HanzhiZhang/lowResSegModel_Object") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7bdb262f85bceed4889b2e92ee1fdde7a312c6ef180a716541b5fb4b569a4588
- Size of remote file:
- 375 MB
- SHA256:
- cc992c4783adf10cd8fcfe68dd69ccbaed8497113ee0baebd84bff74c049d62e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.