Keras
English
remote-sensing
multispectral
hyperspectral
uncertainty-quantification
conformal-prediction
Instructions to use DaneshSelwal/Classification-6Bands-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DaneshSelwal/Classification-6Bands-Models 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://DaneshSelwal/Classification-6Bands-Models") - Notebooks
- Google Colab
- Kaggle
File size: 1,167 Bytes
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language:
- en
license: mit
library_name: keras
tags:
- remote-sensing
- multispectral
- hyperspectral
- uncertainty-quantification
- conformal-prediction
metrics:
- accuracy
- kappa
- coverage
---
# 6-Band Hyperspectral Classification Model Registry
This repository hosts deep neural network checkpoints trained on the 6-band hyperspectral benchmark dataset (330x307x6, 7 classes) for conformal uncertainty quantification and high-precision remote sensing classification.
## Benchmark Architectures
- **AlexNet CNN Backbone**: 6-channel convolutional feature extractor
- **GFNet (Global Filter Network)**: Frequency-domain 2D FFT global spatial interaction network
- **ViT_UNet**: Hybrid Vision Transformer and U-Net encoder-decoder architecture
## Checkpoints Included
- `baseline/models/AlexNet_CNN_best.keras`
- `baseline/models/GFNet_best.keras`
- `baseline/models/ViT_UNet_best.keras`
- `baseline/models/AlexNet_CNN_final.keras`
- `baseline/models/GFNet_final.keras`
- `baseline/models/ViT_UNet_final.keras`
## Authors & Attribution
- **Principal Author**: Danesh Selwal (`selwaldanesh@gmail.com`)
- **Co-authored by**: Claude (`claude@anthropic.com`)
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