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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7531a4d132c49d8a0388c1a03c2449bddb4ede1666fddc9a18d82218ab2c5c38
- Size of remote file:
- 286 kB
- SHA256:
- 4bc59f5d727fe6e757c363af2c46ac9db8ce0ad40b9400d13c682277fee84389
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