Instructions to use adwod/VIT_10000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adwod/VIT_10000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="adwod/VIT_10000") 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("adwod/VIT_10000") model = AutoModelForImageClassification.from_pretrained("adwod/VIT_10000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6683c460c087f6a0e49f232e301a9bbdcc48846846ffe9368cff00ff5fa0f8a0
- Size of remote file:
- 343 MB
- SHA256:
- 9eba5122006b58ce48c38182ca49ab32cf785cf21a9b2e79ae91cb2797fa6994
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.