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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bdf2ebf3d072450630465ce7ddaab595febd56c8a2b4619823b3606b26338545
- Size of remote file:
- 3.05 MB
- SHA256:
- 40559caccca42d521a58b925ce8d0a5c8e31599ee923c203bb38bac5cbfe7345
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