Instructions to use rishitunu/ecc_segformerv1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishitunu/ecc_segformerv1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="rishitunu/ecc_segformerv1")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("rishitunu/ecc_segformerv1") model = SegformerForSemanticSegmentation.from_pretrained("rishitunu/ecc_segformerv1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rishitunu/ecc_segformerv1: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/rishitunu/ecc_segformerv1/resolve/main/training_args.bin
- Command line
-
hf download hf://rishitunu/ecc_segformerv1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rishitunu/ecc_segformerv1/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 03dfa99ad5b92d9187a7877951a5aac736d9331e941627f64794e5f93d385e84
- Size of remote file:
- 4.03 kB
- SHA256:
- ce0e6db1cdb4dfbc2c1cfc3e23350e0b30e4b5251514e7c618108e7ecc0f1a53
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