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 tfjs_model.zip from Nekshay/Finetuned-MobilVIT: direct link, hf CLI and curl.
- Browser
- Download file 22.6 MB
-
https://huggingface.co/Nekshay/Finetuned-MobilVIT/resolve/main/tfjs_model.zip
- Command line
-
hf download hf://Nekshay/Finetuned-MobilVIT/tfjs_model.zip
-
curl -L -o tfjs_model.zip https://huggingface.co/Nekshay/Finetuned-MobilVIT/resolve/main/tfjs_model.zip
22.6 MB
- Xet hash:
- 25088b459c9d7dccb6389ad9196ce53f40685267dda204dcaaba5974857995b0
- Size of remote file:
- 22.6 MB
- SHA256:
- 99c19aa6e027ecd08e340011fe95519363cc6bd5cb5607780c6f39dcb45c2189
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