Instructions to use Ragweed/sidewalk-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Ragweed/sidewalk-segmentation with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Ragweed/sidewalk-segmentation") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Ragweed/sidewalk-segmentation: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/Ragweed/sidewalk-segmentation/resolve/main/README.md
- Command line
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hf download hf://Ragweed/sidewalk-segmentation/README.md
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curl -L -o README.md https://huggingface.co/Ragweed/sidewalk-segmentation/resolve/main/README.md
297 Bytes
metadata
license: mit
library_name: keras
pipeline_tag: image-segmentation
Semantic segmentation model for segmenting sidewalks from other object in an image.
Utilizes U-Net with Resnet34 backbone for transfer learning.
Trained on 512x512 images and expects images with even dimensions.