Instructions to use MRiabov/WireSegHR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MRiabov/WireSegHR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="MRiabov/WireSegHR")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MRiabov/WireSegHR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download src/wireseghr/data/transforms.py from MRiabov/WireSegHR: direct link, hf CLI and curl.
- Browser
- Download file 277 Bytes
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https://huggingface.co/MRiabov/WireSegHR/resolve/main/src/wireseghr/data/transforms.py
- Command line
-
hf download hf://MRiabov/WireSegHR/src/wireseghr/data/transforms.py
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curl -L -o transforms.py https://huggingface.co/MRiabov/WireSegHR/resolve/main/src/wireseghr/data/transforms.py
277 Bytes
| # Training-time transforms: scaling, rotation, flip, photometric distortion | |
| # TODO: Implement deterministic transform composition for reproducibility | |
| class TrainTransforms: | |
| def __init__(self): | |
| pass | |
| def __call__(self, image, mask): | |
| return image, mask | |