Instructions to use hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification 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-MobileNetV2ForImageClassification 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-MobileNetV2ForImageClassification") 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-MobileNetV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-MobileNetV2ForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 967bb9c772e9b48775267afcf07f152cf8ef1e05240ac4c442fd1e23d15301d7
- Size of remote file:
- 1.04 MB
- SHA256:
- 6eee2ffda47fb61dd086a4c53fbaf8cef07e3719eb3ef31c7f1f84bfaa9ff45a
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