Image Segmentation
Keras
ONNX
English
Chinese
Keras
TF-Keras
Safetensors
TensorFlow
biology
agriculture
weeds
vegetation
camouflage
deep learning
imagery
segmentation
medical
forest fire
wildfire
fuel
Instructions to use markrodrigo/vegetation-image-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use markrodrigo/vegetation-image-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://markrodrigo/vegetation-image-segmentation") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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model = keras.models.load_model('../model/image-segmentation-vegetation-1.0.keras')
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model = keras.models.load_model('../model/image-segmentation-vegetation-1.0.h5')
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model.summary(
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print_fn=None,
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expand_nested=False,
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show_trainable=False,
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layer_range=None,)
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**TensorFlow**
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import tensorflow as tf
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model = tf.keras.models.load_model('../model/image-segmentation-vegetation-1.0.keras')
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model.summary(
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layer_range=None,)
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### Evaluation / Accuracy of Target Vegetation
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Rand Index: .92 - .96 (geographic latitude and regional variations)
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model = keras.models.load_model('../model/image-segmentation-vegetation-1.0.keras')
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model.summary(
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line_length=None,
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positions=None,
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layer_range=None,)
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### Evaluation / Accuracy of Target Vegetation
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Rand Index: .92 - .96 (geographic latitude and regional variations)
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