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.kerasbaseline/models/GFNet_best.kerasbaseline/models/ViT_UNet_best.kerasbaseline/models/AlexNet_CNN_final.kerasbaseline/models/GFNet_final.kerasbaseline/models/ViT_UNet_final.keras
Authors & Attribution
- Principal Author: Danesh Selwal (
selwaldanesh@gmail.com) - Co-authored by: Claude (
claude@anthropic.com)
- Downloads last month
- 119
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
# !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")