Instructions to use hf-internal-testing/tiny-random-ViTMSNForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ViTMSNForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-ViTMSNForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ViTMSNForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a19e72ecd353bf5a4bac6db38dcbdf0cfd2b540d6c1cf2268fc05cff662d73f9
- Size of remote file:
- 254 kB
- SHA256:
- 827509b28efff2de0683ee21b8698222577f4f95873de3c2d7b8c681d81c7eed
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