Instructions to use rishitunu/ecc_segformerv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishitunu/ecc_segformerv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="rishitunu/ecc_segformerv3")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("rishitunu/ecc_segformerv3") model = SegformerForSemanticSegmentation.from_pretrained("rishitunu/ecc_segformerv3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rishitunu/ecc_segformerv3: direct link, hf CLI and curl.
- Browser
- Download file 339 MB
-
https://huggingface.co/rishitunu/ecc_segformerv3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rishitunu/ecc_segformerv3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rishitunu/ecc_segformerv3/resolve/main/pytorch_model.bin
339 MB
- Xet hash:
- 42171efee67e088b37d2640cdc174267053e0017ef4c06baead7cf56c0141347
- Size of remote file:
- 339 MB
- SHA256:
- de9e2dcb663a36696603f3f556ec203507bc834f7e1b2f6f3251e488ebff89e8
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