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:
- 6498c653721a5519abcb6d8f637c3fb303951395a6953e9a1ec17a681d1bc318
- Size of remote file:
- 1.84 MB
- SHA256:
- 46d2b5389b335e9878fccd312223e6e9027b339374267a9e533c5254debc2048
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