Instructions to use hf-tiny-model-private/tiny-random-ViTMSNForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ViTMSNForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-ViTMSNForImageClassification") 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("hf-tiny-model-private/tiny-random-ViTMSNForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-ViTMSNForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d8bed35f1ba145da390d6883eb0113ee96840d356d3d83056ec81704628d048e
- Size of remote file:
- 195 kB
- SHA256:
- f236889bbfce14d62b507b87b8b97e07bfbff58958b39b06502a010a4f505620
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