Instructions to use whyoke/segmentation_model_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use whyoke/segmentation_model_test with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("whyoke/segmentation_model_test") model = SegformerForSemanticSegmentation.from_pretrained("whyoke/segmentation_model_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from whyoke/segmentation_model_test: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/whyoke/segmentation_model_test/resolve/main/training_args.bin
- Command line
-
hf download hf://whyoke/segmentation_model_test/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/whyoke/segmentation_model_test/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 5f7b6e18a0ae202f0931f5d03c9f1cf51c4b91750e54de5bacb35d4d30308309
- Size of remote file:
- 5.24 kB
- SHA256:
- 2676a379e5b51b99c139c608fcb10dbf84b7b13ab29171fdb6c252eba69a2b0a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.