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.h5 from iamabhayaditya/InceptionResNetV2-UNet-MBRSC: direct link, hf CLI and curl.
- Browser
- Download file 444 MB
-
https://huggingface.co/iamabhayaditya/InceptionResNetV2-UNet-MBRSC/resolve/main/InceptionResNetV2-UNet.h5
- Command line
-
hf download hf://iamabhayaditya/InceptionResNetV2-UNet-MBRSC/InceptionResNetV2-UNet.h5
-
curl -L -o InceptionResNetV2-UNet.h5 https://huggingface.co/iamabhayaditya/InceptionResNetV2-UNet-MBRSC/resolve/main/InceptionResNetV2-UNet.h5
444 MB
- Xet hash:
- a6afc48c31ff189fe44baea70f55e08fcb8d05ca5475c8885e44a41c5b3ff40d
- Size of remote file:
- 444 MB
- SHA256:
- 8156e7688eb218b208118be2bdda8d7fff3820f2ed6bc4ee978256aff92ff8ea
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