Instructions to use ShuaHousetable/condition-Kitchen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShuaHousetable/condition-Kitchen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ShuaHousetable/condition-Kitchen") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ShuaHousetable/condition-Kitchen") model = AutoModelForImageClassification.from_pretrained("ShuaHousetable/condition-Kitchen") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6367f817be5a8805c9d7c13a48176b9929084eb4b5a18bb9f5944f4b9ba1ad51
- Size of remote file:
- 110 MB
- SHA256:
- 1b596983e4d210ed82f10cb03c72dd1f25c1fda2a3d205fe51be69d72f54030a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.