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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1372696d49a9a35fdca842a122e875e7c0da6a5a61c336464c7cfd50371aab24
- Size of remote file:
- 1.86 MB
- SHA256:
- 6bb233c694b728dcd93668606361da1bf7be8bf6ddf6bbfa3addc3aaccc432d0
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