Instructions to use nqvii/deit_fold_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqvii/deit_fold_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nqvii/deit_fold_5") 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("nqvii/deit_fold_5") model = AutoModelForImageClassification.from_pretrained("nqvii/deit_fold_5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50f3870ce45e7f0df7fe55053f5765f1dee85a45ee1eeb97d3710a01bbfb6481
- Size of remote file:
- 5.2 kB
- SHA256:
- 9b1dd7829dfa886566727f68de4301843b830f71c7550e77748a2b94470d7691
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.