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 model.pb from Nekshay/Finetuned-MobilVIT: direct link, hf CLI and curl.
- Browser
- Download file 258 MB
-
https://huggingface.co/Nekshay/Finetuned-MobilVIT/resolve/main/model.pb
- Command line
-
hf download hf://Nekshay/Finetuned-MobilVIT/model.pb
-
curl -L -o model.pb https://huggingface.co/Nekshay/Finetuned-MobilVIT/resolve/main/model.pb
258 MB
- Xet hash:
- 3d878b66cc8da0cb10955d6867c2d8170d0dfc903c8271b6ba7da7e8b3602352
- Size of remote file:
- 258 MB
- SHA256:
- 35fc02ba4fa3b9a9feec8754f1348b1f223d61cb53159553a92397d409ab18fd
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