Instructions to use KodaPop/begone-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KodaPop/begone-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="KodaPop/begone-model") 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("KodaPop/begone-model") model = AutoModelForImageClassification.from_pretrained("KodaPop/begone-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4be891e2111b206d777f3ec2e6419dad0b18497840aa8ab578d246bc09162f2e
- Size of remote file:
- 5.84 kB
- SHA256:
- f8200b57ef22ccedc7270acd5e2bf750a06e113274f0668903b9deb13d31d5ff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.