Instructions to use hf-tiny-model-private/tiny-random-RegNetForImageClassification 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-RegNetForImageClassification 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-RegNetForImageClassification") 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-RegNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-RegNetForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd2fffb03b6ec4d4c033d6eeab5657573efc8ddb20e45ed70814704b167748be
- Size of remote file:
- 352 kB
- SHA256:
- f8cd0cea920f53cd217eed5a53d339259770abb9b4daa16e9fde0fa07ffcfe54
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