Instructions to use nielsr/sidewalk-semantic-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nielsr/sidewalk-semantic-demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="nielsr/sidewalk-semantic-demo")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("nielsr/sidewalk-semantic-demo") model = SegformerForSemanticSegmentation.from_pretrained("nielsr/sidewalk-semantic-demo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nielsr/sidewalk-semantic-demo: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/nielsr/sidewalk-semantic-demo/resolve/main/training_args.bin
- Command line
-
hf download hf://nielsr/sidewalk-semantic-demo/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nielsr/sidewalk-semantic-demo/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- bea57743f489ebe806f99f15074cdc9b14564e2652b23a451d77b34e0edeecbf
- Size of remote file:
- 3.06 kB
- SHA256:
- fc1b81e389b876fe2b090e45731769f6c712812e8e548954fb0a6856d7e930c3
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