Image Classification
Keras
English
computer-vision
satellite-imagery
remote-sensing
xview
efficientnet
ensemble
transfer-learning
tensorflow
Instructions to use MelenL/EfficientNetB0_Ensemble_for_Satellite_Image_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use MelenL/EfficientNetB0_Ensemble_for_Satellite_Image_Classification 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://MelenL/EfficientNetB0_Ensemble_for_Satellite_Image_Classification") - Notebooks
- Google Colab
- Kaggle
EfficientNetB0_Ensemble_for_Satellite_Image_Classification / Report_ImageRecognitionAndObjectDetectiononthexViewSatelliteDataset.pdf
Download Report_ImageRecognitionAndObjectDetectiononthexViewSatelliteDataset.pdf from MelenL/EfficientNetB0_Ensemble_for_Satellite_Image_Classification: direct link, hf CLI and curl.
- Browser
- Download file 10.5 MB
-
https://huggingface.co/MelenL/EfficientNetB0_Ensemble_for_Satellite_Image_Classification/resolve/main/Report_ImageRecognitionAndObjectDetectiononthexViewSatelliteDataset.pdf
- Command line
-
hf download hf://MelenL/EfficientNetB0_Ensemble_for_Satellite_Image_Classification/Report_ImageRecognitionAndObjectDetectiononthexViewSatelliteDataset.pdf
-
curl -L -o Report_ImageRecognitionAndObjectDetectiononthexViewSatelliteDataset.pdf https://huggingface.co/MelenL/EfficientNetB0_Ensemble_for_Satellite_Image_Classification/resolve/main/Report_ImageRecognitionAndObjectDetectiononthexViewSatelliteDataset.pdf
10.5 MB
- Xet hash:
- ca0405a62921b3cb5afe1afd86a22c6d37b3953aea0d2ebc3e53fb3f668908b1
- Size of remote file:
- 10.5 MB
- SHA256:
- ad6979faaf57eaad30745fed1b4427f7ca704d1e1b82c3589a505a6d6214fca0
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