Instructions to use Luuu01/ModelDone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Luuu01/ModelDone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Luuu01/ModelDone") 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("Luuu01/ModelDone") model = AutoModelForImageClassification.from_pretrained("Luuu01/ModelDone", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8cb055dc118e580d63237b8e07b03fc755d7916d948ba57e1c94a436f2023952
- Size of remote file:
- 111 MB
- SHA256:
- ab9db9e616d716dd912b6026ed5818c28c41dbd1f375bc2389eee7a6e46dc842
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.