Instructions to use Mattysmittttt/camonet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mattysmittttt/camonet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Mattysmittttt/camonet") 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("Mattysmittttt/camonet") model = AutoModelForImageClassification.from_pretrained("Mattysmittttt/camonet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5b3d169b963fd0111f91eb858ae4ed3a3848ab8552c4f0b9ec3c94c40e0ff8c6
- Size of remote file:
- 88.4 MB
- SHA256:
- f031d15334bc1698fb596012bd81714f63fd681fd59d5449676adf48a646df9c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.