Instructions to use apple/mobilevitv2-1.0-imagenet1k-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apple/mobilevitv2-1.0-imagenet1k-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="apple/mobilevitv2-1.0-imagenet1k-256") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, MobileViTv2ForImageClassification processor = AutoImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256") model = MobileViTv2ForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80e93475d4868417430254a020132fbea639a79236b38a9abfb910c0fab67f66
- Size of remote file:
- 19.8 MB
- SHA256:
- 895b830a71a8cce260b2f45d2691d1bd075e843b9d5182f778b5c07d23ae2933
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