Instructions to use Nekshay/Finetuned-MobilVIT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nekshay/Finetuned-MobilVIT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Nekshay/Finetuned-MobilVIT") 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("Nekshay/Finetuned-MobilVIT") model = AutoModelForImageClassification.from_pretrained("Nekshay/Finetuned-MobilVIT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download production1.zip from Nekshay/Finetuned-MobilVIT: direct link, hf CLI and curl.
- Browser
- Download file 77.2 kB
-
https://huggingface.co/Nekshay/Finetuned-MobilVIT/resolve/main/production1.zip
- Command line
-
hf download hf://Nekshay/Finetuned-MobilVIT/production1.zip
-
curl -L -o production1.zip https://huggingface.co/Nekshay/Finetuned-MobilVIT/resolve/main/production1.zip
77.2 kB
- Xet hash:
- eff90f46cd4394da78574c28a4f60c47001f2a75be954e87233a592cb64a8d82
- Size of remote file:
- 77.2 kB
- SHA256:
- 00c36fdf186f4a46e47cfe530e902f047ed4b676b71fbca1539f5d097ab54f76
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