Instructions to use Frodnar/bee-likes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Frodnar/bee-likes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Frodnar/bee-likes") 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("Frodnar/bee-likes") model = AutoModelForImageClassification.from_pretrained("Frodnar/bee-likes", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- def4dc8988f563647585ba3e6e349c27a994e83aed2b5e341aba65a0a7dc781b
- Size of remote file:
- 343 MB
- SHA256:
- d2921cbf187a5df1722ef7566ea88e33467f1ca02dfbcb402a565ebeedb6b4a6
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