Instructions to use AI-Lab-Makerere/mit-b0_corm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI-Lab-Makerere/mit-b0_corm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="AI-Lab-Makerere/mit-b0_corm")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("AI-Lab-Makerere/mit-b0_corm") model = SegformerForSemanticSegmentation.from_pretrained("AI-Lab-Makerere/mit-b0_corm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AI-Lab-Makerere/mit-b0_corm: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/AI-Lab-Makerere/mit-b0_corm/resolve/main/training_args.bin
- Command line
-
hf download hf://AI-Lab-Makerere/mit-b0_corm/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AI-Lab-Makerere/mit-b0_corm/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 88c6d4b718c22e63c22513ec2f83360ecd35104e06ecf4582ee7dae62082952d
- Size of remote file:
- 5.18 kB
- SHA256:
- 800b12cc8c19467046cb454360ccb3dc470b43da15a8f46a603316961a95edca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.