Instructions to use hf-internal-testing/tiny-random-SegformerForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SegformerForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-SegformerForImageClassification") 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-SegformerForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-SegformerForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 78ad4710348c4ab477505fbd929c7bc6d0ef13443851255c58a0d948ee303cb2
- Size of remote file:
- 3.01 MB
- SHA256:
- 57ee8eda55146666871fbd5384b1947f14913c1e2edf10f3e7d0f69ff321612b
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