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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a8da732e69adc98a634b574fe8a112bd9c686f99e15e97299d4de815eb31de97
- Size of remote file:
- 387 kB
- SHA256:
- f692af1e23990e2801fe3616e22778f96994af6407e44de4d105dcd53a463825
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