Instructions to use hf-internal-testing/tiny-random-ConvNextV2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ConvNextV2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-ConvNextV2ForImageClassification") 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-ConvNextV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ConvNextV2ForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c019babdaaa9f23f0b732ff426a25c7a52d442d723463bbcad3f247ae33824c0
- Size of remote file:
- 442 kB
- SHA256:
- 293b31f47ea3d3e704c3b58eb10c06779a36047b1ce09853809e80945fdde1a8
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