Instructions to use hf-tiny-model-private/tiny-random-SegformerForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-SegformerForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/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-tiny-model-private/tiny-random-SegformerForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-SegformerForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9df114ccccb20c6b8fb58a653d3a347553ee911b0db3b25cc31df111e5e6e86c
- Size of remote file:
- 3.01 MB
- SHA256:
- 3462bbe3aeab8f3137fc3ba4c28b8739ab57d435e4e6ad7eb154e6157c0673c0
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