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:
- 34e9ecf3b029e29746d86630d67857f072ecb806f6a241b889cdccb2fef8b6e6
- Size of remote file:
- 303 kB
- SHA256:
- 095f2d1bfad7e8e7cec4d3d442a706b4e34b32ceb8c971c5d18e2854dcdbbd14
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