Instructions to use andro-flock/b2-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use andro-flock/b2-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="andro-flock/b2-classification") 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("andro-flock/b2-classification") model = AutoModelForImageClassification.from_pretrained("andro-flock/b2-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a24ba7f87c3513281c1b7dfbc0617a8a2255f3341dbd0bfc9f84042604888b1
- Size of remote file:
- 96.9 MB
- SHA256:
- 69bc8d385ea1d594e00963b6adf9c45f67af93990a43d7cf9b802a6ed23371e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.