Instructions to use hf-tiny-model-private/tiny-random-MobileViTForImageClassification 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-MobileViTForImageClassification 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-MobileViTForImageClassification") 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-MobileViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-MobileViTForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a8b063d4403f56f942d53924d5333e30d727644893e4a74c51d3a620a179b00
- Size of remote file:
- 19.9 MB
- SHA256:
- c09da8e104f288f4994bc9b96f87c3e739b5175fc0f77d157297d563ff4aecaa
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