Image Segmentation
Keras
LiteRT
English
satellite-imagery
earth-observation
remote-sensing
semantic-segmentation
u-net
inceptionresnetv2
edge-ai
post-training-quantization
Instructions to use iamabhayaditya/InceptionResNetV2-UNet-MBRSC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use iamabhayaditya/InceptionResNetV2-UNet-MBRSC 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://iamabhayaditya/InceptionResNetV2-UNet-MBRSC") - Notebooks
- Google Colab
- Kaggle
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README.md
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predictions = interpreter.get_tensor(output_details[0]['index'])
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pred_mask = np.argmax(predictions[0], axis=-1)
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```
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## 📜 Lineage & Attribution
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This project is derived from the open-source implementation by [Ayush Dabra](https://github.com/ayushdabra/dubai-satellite-imagery-segmentation) licensed under **GNU General Public License v3.0 (GPL-3.0)**.
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predictions = interpreter.get_tensor(output_details[0]['index'])
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pred_mask = np.argmax(predictions[0], axis=-1)
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```
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**GitHub Repo Link: https://github.com/imadi124/Drishti**
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## 📜 Lineage & Attribution
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This project is derived from the open-source implementation by [Ayush Dabra](https://github.com/ayushdabra/dubai-satellite-imagery-segmentation) licensed under **GNU General Public License v3.0 (GPL-3.0)**.
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