Instructions to use hf-internal-testing/tiny-random-FocalNetForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FocalNetForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-FocalNetForImageClassification") 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-FocalNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-FocalNetForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 58f125af865ce7175b68b2b014875bdb6ed5c0747fb2c88ca9bde6b77af2e495
- Size of remote file:
- 303 kB
- SHA256:
- bc024a36aeeb81383a6e8c57a6a96187bc1d0c2299a7f437212b3c23e63500ad
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