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
Download InceptionResNetV2-UNet.weights.h5 from iamabhayaditya/InceptionResNetV2-UNet-MBRSC: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/iamabhayaditya/InceptionResNetV2-UNet-MBRSC/resolve/main/InceptionResNetV2-UNet.weights.h5
- Command line
-
hf download hf://iamabhayaditya/InceptionResNetV2-UNet-MBRSC/InceptionResNetV2-UNet.weights.h5
-
curl -L -o InceptionResNetV2-UNet.weights.h5 https://huggingface.co/iamabhayaditya/InceptionResNetV2-UNet-MBRSC/resolve/main/InceptionResNetV2-UNet.weights.h5
443 MB
- Xet hash:
- d08f7081d638348edb7f7de08b7e9309fc6c2ddd4d57f5c3731a10ca8736cdf0
- Size of remote file:
- 443 MB
- SHA256:
- 72d29ce544e768b8d06366df9397385be98b79cbfdca31acd7d05dcee8079cbe
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