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") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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---
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license: other
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tags:
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- generated_from_trainer
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- image_segmentation
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model-index:
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- name: segformer-b0-finetuned-segments-sidewalk
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results: []
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datasets:
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- segments/sidewalk-semantic
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library_name: transformers
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pipeline_tag: image-segmentation
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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license: other
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library_name: transformers
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tags:
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- generated_from_trainer
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- image_segmentation
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datasets:
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- segments/sidewalk-semantic
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pipeline_tag: image-segmentation
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base_model: nvidia/mit-b0
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model-index:
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- name: segformer-b0-finetuned-segments-sidewalk
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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