Instructions to use varcoder/Augmented-MIT-b5-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varcoder/Augmented-MIT-b5-new with Transformers:
# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("varcoder/Augmented-MIT-b5-new") model = SegformerForSemanticSegmentation.from_pretrained("varcoder/Augmented-MIT-b5-new", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77262bb8c54af4db07f9f0d53fe2c58c5ca1b8f05606726100cd2072c7e33faf
- Size of remote file:
- 339 MB
- SHA256:
- 7aeb90311f71b0abc974fd507551daad45ecc681e5234906ad6f4e33c0bcf357
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.