Instructions to use hf-internal-testing/tiny-random-PoolFormerForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PoolFormerForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-PoolFormerForImageClassification") 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-PoolFormerForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-PoolFormerForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a336a5d1d720c7fbf1ef2b2fdd4b0e49a0372a0b6d497a2ebe593088f2469ef8
- Size of remote file:
- 1.82 MB
- SHA256:
- faf5fd864665d2c2ebf8fa78eaca94134818b3c48de9c89dce73d556cfa3bf9e
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