Instructions to use rishitunu/FINAL_ecc_segformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishitunu/FINAL_ecc_segformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="rishitunu/FINAL_ecc_segformer")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("rishitunu/FINAL_ecc_segformer") model = SegformerForSemanticSegmentation.from_pretrained("rishitunu/FINAL_ecc_segformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rishitunu/FINAL_ecc_segformer: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/rishitunu/FINAL_ecc_segformer/resolve/main/training_args.bin
- Command line
-
hf download hf://rishitunu/FINAL_ecc_segformer/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/rishitunu/FINAL_ecc_segformer/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 78c29489e135cc506d2e5c72ff1811c1792f71bb42529e65e19ec9baaf3cc364
- Size of remote file:
- 4.54 kB
- SHA256:
- 7c1cef15d940a0acd23c2ba6683e4efd3da50e190599933a3f6568f06b44bb9c
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