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:
- 1170f1becacbdccf15dbb10bfa0e68062934dcbe7df2c5f266172a3bc8abc30f
- Size of remote file:
- 321 kB
- SHA256:
- 436710c00d9a67fd63861b165a1ad5ad7ff4f83944f9b6e7b71370a1ba5adbfa
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