Instructions to use hf-tiny-model-private/tiny-random-PoolFormerForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-PoolFormerForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/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-tiny-model-private/tiny-random-PoolFormerForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-PoolFormerForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bafae5b4979d3b4e0ed5a1e33b65fa5728b5ff12f7851018b1de9bf6c22c03a
- Size of remote file:
- 1.82 MB
- SHA256:
- 84c65dd8ee26de6bf71276ba4c2b69b09cf303406d48f1647b409caad8fe65d2
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