Instructions to use varcoder/segformer-b4-crack-segmentation-dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varcoder/segformer-b4-crack-segmentation-dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="varcoder/segformer-b4-crack-segmentation-dataset")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("varcoder/segformer-b4-crack-segmentation-dataset") model = SegformerForSemanticSegmentation.from_pretrained("varcoder/segformer-b4-crack-segmentation-dataset") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#7 opened over 2 years ago
by
librarian-bot
Adding `safetensors` variant of this model
#6 opened over 2 years ago
by
SFconvertbot
Adding `safetensors` variant of this model
#5 opened over 2 years ago
by
SFconvertbot
Adding `safetensors` variant of this model
#4 opened over 2 years ago
by
SFconvertbot
Adding `safetensors` variant of this model
#3 opened over 2 years ago
by
SFconvertbot
Adding `safetensors` variant of this model
#2 opened almost 3 years ago
by
SFconvertbot
Adding `safetensors` variant of this model
#1 opened almost 3 years ago
by
SFconvertbot