Instructions to use musfik41/broccoli_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use musfik41/broccoli_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="musfik41/broccoli_detection") 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("musfik41/broccoli_detection") model = AutoModelForImageClassification.from_pretrained("musfik41/broccoli_detection") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 836c1128fff268b5ed8ff897ede0c54dd42d4ad18c17bedac99267de0611bac6
- Size of remote file:
- 343 MB
- SHA256:
- 99d3d57d2d12482a6b72143f0eaf313cf08cacd26ddddd937c53e6b50458aabf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.