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")# 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
- Xet hash:
- 586219a19291fa4eb9c0a6624361e768198c4f978779e68ba31b21a9870418a0
- Size of remote file:
- 4.03 kB
- SHA256:
- ba04c538e3486fc0bac16eefdd20887ce1c6667a41f59d2666e478049fce0b31
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.