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:
- f9b274a169d9ce2489fa1e1a8fe40957edffb7f40efbfbd966ff4a52acf8ce0d
- Size of remote file:
- 3.25 MB
- SHA256:
- 2473afce8248c00e8c33e099b11edf77856536a1d7e62fcefe65a7e4a6dcf0a2
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