Instructions to use ivensamdh/beitv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ivensamdh/beitv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ivensamdh/beitv2") 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("ivensamdh/beitv2") model = AutoModelForImageClassification.from_pretrained("ivensamdh/beitv2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd7316b9c561ff7137d1fb3789d05c7d89cd70a40c72a1c5e8404b766ac5be89
- Size of remote file:
- 3.52 kB
- SHA256:
- 713194ea275ae136a34289ea465c2ff6f0d46526a06cdd17b4a9d62d5aa90bb9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.