Instructions to use varcoder/CrackSeg-MIT-b0-dice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varcoder/CrackSeg-MIT-b0-dice with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("varcoder/CrackSeg-MIT-b0-dice") model = SegformerForSemanticSegmentation.from_pretrained("varcoder/CrackSeg-MIT-b0-dice", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from varcoder/CrackSeg-MIT-b0-dice: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/varcoder/CrackSeg-MIT-b0-dice/resolve/84516daebfec147be6341bbaf3e80d0bd16d1a16/training_args.bin
- Command line
-
hf download hf://varcoder/CrackSeg-MIT-b0-dice@84516daebfec147be6341bbaf3e80d0bd16d1a16/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/varcoder/CrackSeg-MIT-b0-dice/resolve/84516daebfec147be6341bbaf3e80d0bd16d1a16/training_args.bin
4.03 kB
- Xet hash:
- 47276badbb91638f794fc3dd3c1c8fa588a9c3619db0472e5b7a1bbb7e361201
- Size of remote file:
- 4.03 kB
- SHA256:
- 320eaa15b0a31b50dc11962d04192bee896c04b99033f919f6aa035c1c3fadaf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.