Instructions to use rishitunu/ecc_segformerv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishitunu/ecc_segformerv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="rishitunu/ecc_segformerv2")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("rishitunu/ecc_segformerv2") model = SegformerForSemanticSegmentation.from_pretrained("rishitunu/ecc_segformerv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rishitunu/ecc_segformerv2: direct link, hf CLI and curl.
- Browser
- Download file 339 MB
-
https://huggingface.co/rishitunu/ecc_segformerv2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rishitunu/ecc_segformerv2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rishitunu/ecc_segformerv2/resolve/main/pytorch_model.bin
339 MB
- Xet hash:
- 9cd66759bd17931f412612e7c51d7d968651c0512886282e9a621eb8ac565b26
- Size of remote file:
- 339 MB
- SHA256:
- 96c82428684dc75ea9c5d6486a42456f546e3fc48fd19039b2700d35fb18d611
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