Instructions to use darklorddad/Model-Focalnet-Base-82 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use darklorddad/Model-Focalnet-Base-82 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="darklorddad/Model-Focalnet-Base-82") 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("darklorddad/Model-Focalnet-Base-82") model = AutoModelForImageClassification.from_pretrained("darklorddad/Model-Focalnet-Base-82", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75112b07ed61379ca2d101bcd9fc45a56e6c94b03a3f5e385a360400f033e64a
- Size of remote file:
- 5.3 kB
- SHA256:
- 24779ba161a24fcf404cfce49654b0c18640007f7086a06316de954621441816
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.