HyperFM250k_subset
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HyperFM Dataset Overview This dataset contains **4,250 hyperspectral samples** from NASA PACE mission, curated for machine learning and computer vision research. It is designed to support tasks such as regression, segmentation, and representation learning using hyperspectral imaging (HSI) data. A larger version of this dataset (**3TB+**) can be generated using the provided codebase (link below). Directory Structure .├── hsi/├── target/├── train_list_2k.csv├── valid_list_250.csv├── test_list_2k.csv Description - **hsi/** Contains hyperspectral data samples. Each file corresponds to a single observation. Dim: (96,96,291) - **target/** Contains labels or target values associated with each HSI sample. Four (4) Regression Tasks: *cld_xxx.npy [Dim: (96,96,4)]. One (1) Segmentation Task: *cldmask_xxx.npy [Dim: (96,96)] - **train_list_2k.csv** Training split with ~2,000 samples. - **valid_list_250.csv** Validation split with ~250 samples. - **test_list_2k.csv** Test split with ~2,000 samples. Each CSV file defines the mapping between input samples and their corresponding targets. Dataset Statistics - Total samples: **4,250**- Training set: ~2,000 samples - Validation set: ~250 samples - Test set: ~2,000 samples Full Dataset Generation A significantly larger dataset (**3TB+**) can be generated using the official codebase: - GitHub repository: *https://github.com/umbc-sanjaylab/HyperFM* The repository includes scripts for:- Data preprocessing- Hyperspectral cube generation- Label construction- Dataset scaling Associated Publication - Paper: Zahid Hassan Tushar, and Sanjay Purushotham, "HyperFM: A Efficient Hyperspectral Foundation Model with Spectral Grouping", CVPR 2026 (findings). Please cite the paper if you use this dataset in your research. Usage Notes - Ensure consistent preprocessing across splits when training models.- Refer to the CSV files for correct pairing of inputs and targets.- Large-scale experiments are recommended using the full generated dataset. License CC BY 4.0 Contact For questions, issues, or collaboration: - **Zahid Hassan Tushar** Email: ztushar1@umbc.edu - **Sanjay Purushotham** Email: psanjay@umbc.edu Acknowledgments If you use this dataset, please acknowledge the authors and associated publication.
