Image Segmentation
Transformers
PyTorch
TensorBoard
segformer
Generated from Trainer
image_segmentation
Instructions to use iammartian0/RoadSense_High_Definition_Street_Segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iammartian0/RoadSense_High_Definition_Street_Segmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="iammartian0/RoadSense_High_Definition_Street_Segmentation")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("iammartian0/RoadSense_High_Definition_Street_Segmentation") model = SegformerForSemanticSegmentation.from_pretrained("iammartian0/RoadSense_High_Definition_Street_Segmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from iammartian0/RoadSense_High_Definition_Street_Segmentation: direct link, hf CLI and curl.
- Browser
- Download file 15 MB
-
https://huggingface.co/iammartian0/RoadSense_High_Definition_Street_Segmentation/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://iammartian0/RoadSense_High_Definition_Street_Segmentation/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/iammartian0/RoadSense_High_Definition_Street_Segmentation/resolve/main/pytorch_model.bin
15 MB
- Xet hash:
- 3af7ef07d6244ee470c3cb56775a806546cb5045e981775fd49c22b54ba57173
- Size of remote file:
- 15 MB
- SHA256:
- c72f590a662ff70b36aa3702155893765e2283449d56de095e3d1634afc78e1f
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