Instructions to use hf-tiny-model-private/tiny-random-MobileNetV1ForImageClassification 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-MobileNetV1ForImageClassification 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-MobileNetV1ForImageClassification") 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-MobileNetV1ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-MobileNetV1ForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c1c8b9dae16db176bece8c851eda1f20230f7c41328f865e000f651684ee6931
- Size of remote file:
- 928 kB
- SHA256:
- 842c626424ff9c34be1675561f4a9eb4e9a307b7c3568d575607d085a19e5a23
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