Instructions to use hf-internal-testing/tiny-random-ConvNextForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConvNextForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-ConvNextForImageClassification") 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-internal-testing/tiny-random-ConvNextForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ConvNextForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d24f9bf90982fc824cb814d2fd0b51838f909671af1d7c6c7ad3c961cfd1bff9
- Size of remote file:
- 340 kB
- SHA256:
- c5b12f45e5c6f32c08d866c9034f6aec4ee021edf9334e125235f80512a040f2
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