Instructions to use hf-internal-testing/tiny-random-Swinv2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Swinv2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-Swinv2ForImageClassification") 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-Swinv2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-Swinv2ForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6397d0a7308ac61a32cc7324befdd8aa63bdd17449fbe1f8bb0ca13bc009c371
- Size of remote file:
- 310 kB
- SHA256:
- b3d18506080a713fcbe22c43e9ea2682764b32b749bf1897ccbc205b221ca514
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