Instructions to use as-cle-bert/bcus-class-segformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use as-cle-bert/bcus-class-segformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="as-cle-bert/bcus-class-segformer") 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("as-cle-bert/bcus-class-segformer") model = AutoModelForImageClassification.from_pretrained("as-cle-bert/bcus-class-segformer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 453d21f00680d34f9401da55ea3a078406495afce331c2059069327b1cf60bd7
- Size of remote file:
- 5.05 kB
- SHA256:
- d8c20771870e564a7d4eb4b29bce6de9e0da4f5fa3c7e05858a1873e5f94162c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.