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 pytorch_model.bin from rishitunu/FINAL_ecc_segformer: direct link, hf CLI and curl.
- Browser
- Download file 339 MB
-
https://huggingface.co/rishitunu/FINAL_ecc_segformer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rishitunu/FINAL_ecc_segformer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rishitunu/FINAL_ecc_segformer/resolve/main/pytorch_model.bin
339 MB
- Xet hash:
- e4e0f457754d584fe2d0bc6544e58725cdabb99c8fe0c10b086c20c211389cd1
- Size of remote file:
- 339 MB
- SHA256:
- c922615c86bedb676dd0f502fa0525e30307fb85331948dc3bc5107d45315a3c
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