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")# pip install -U transformers accelerate # 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 pytorch_model.bin from Nekshay/Finetuned-MobilVIT: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/Nekshay/Finetuned-MobilVIT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Nekshay/Finetuned-MobilVIT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Nekshay/Finetuned-MobilVIT/resolve/main/pytorch_model.bin
20 MB
- Xet hash:
- 54f8119c0eb6e17877b28c9978d79a68896fb0d39bd17b6aa317cdf91edb3112
- Size of remote file:
- 20 MB
- SHA256:
- 2a38cd756aa077adb6f9a364e48a68cce609c689be9d39d2364e57210cde5665
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