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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ef34de731f960d6c18e6e3eafa5a2e03bf1e5130489119e111262225f384a39
- Size of remote file:
- 330 kB
- SHA256:
- f1c761380d5ffa44b115e149ee2df5b3a0e475bb26e722fd159abbbba3f9ea33
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