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 Vendor_Pred 2.zip from Nekshay/Finetuned-MobilVIT: direct link, hf CLI and curl.
- Browser
- Download file 6.55 MB
-
https://huggingface.co/Nekshay/Finetuned-MobilVIT/resolve/main/Vendor_Pred%202.zip
- Command line
-
hf download 'hf://Nekshay/Finetuned-MobilVIT/Vendor_Pred 2.zip'
-
curl -L -o 'Vendor_Pred 2.zip' https://huggingface.co/Nekshay/Finetuned-MobilVIT/resolve/main/Vendor_Pred%202.zip
6.55 MB
- Xet hash:
- 0a944cf73344b0b19693b476d62f6d38281ba47e4d69e37ec4c5bbc865112d0a
- Size of remote file:
- 6.55 MB
- SHA256:
- e8c351bd5fa026ece6dc8832c6a176b5d0de259f1fe1a4434905b967e0ed696a
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