Instructions to use jackie68/semantic_segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jackie68/semantic_segmentation with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("jackie68/semantic_segmentation") model = SegformerForSemanticSegmentation.from_pretrained("jackie68/semantic_segmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jackie68/semantic_segmentation: direct link, hf CLI and curl.
- Browser
- Download file 15.1 MB
-
https://huggingface.co/jackie68/semantic_segmentation/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jackie68/semantic_segmentation/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jackie68/semantic_segmentation/resolve/main/pytorch_model.bin
15.1 MB
- Xet hash:
- 0c2beda702bf22ac1f3fab7bd240c6095b32bef41ea019c55c4b42cf5c0e5eed
- Size of remote file:
- 15.1 MB
- SHA256:
- d5a74b403d208448d95e79f6692ad542a8b4ac2f54500660408433123f801b88
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.