Instructions to use rishitunu/ecc_segformerv5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishitunu/ecc_segformerv5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("rishitunu/ecc_segformerv5") model = SegformerForSemanticSegmentation.from_pretrained("rishitunu/ecc_segformerv5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rishitunu/ecc_segformerv5: direct link, hf CLI and curl.
- Browser
- Download file 339 MB
-
https://huggingface.co/rishitunu/ecc_segformerv5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rishitunu/ecc_segformerv5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rishitunu/ecc_segformerv5/resolve/main/pytorch_model.bin
339 MB
- Xet hash:
- 427a99e6ee7f7f43ca42b9f04ebbb94cf5e343bc3a85d82a6696be9813790334
- Size of remote file:
- 339 MB
- SHA256:
- e9403c34ffc359376eb15dc60b65cb23282c6c81ec7ad8d8dca460317a39607c
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