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 training_args.bin from rishitunu/ecc_segformerv3: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/rishitunu/ecc_segformerv3/resolve/main/training_args.bin
- Command line
-
hf download hf://rishitunu/ecc_segformerv3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rishitunu/ecc_segformerv3/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- a2c0d284b3afb6ea0c17e5bdb12d03447dde142c6b30bff5f6b3480d7accb4d8
- Size of remote file:
- 4.03 kB
- SHA256:
- fecbf4e9f82ba225dd968ead3cd57ff82e372a74e2ff6749d3910585c2e1ebad
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