metadata

language:
  - en
size_categories:
  - 10K<n<100K
task_categories:
  - image-classification
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': annual crop land
            '1': forest
            '2': brushland or shrubland
            '3': highway or road
            '4': industrial buildings or commercial buildings
            '5': pasture land
            '6': permanent crop land
            '7': residential buildings or homes or apartments
            '8': river
            '9': lake or sea
  splits:
    - name: train
      num_bytes: 70654720.8
      num_examples: 21600
    - name: test
      num_bytes: 8626053.5
      num_examples: 2700
    - name: contrast
      num_bytes: 2577694.4
      num_examples: 2700
    - name: gaussian_noise
      num_bytes: 4344552.8
      num_examples: 2700
    - name: impulse_noise
      num_bytes: 4551961.4
      num_examples: 2700
    - name: jpeg_compression
      num_bytes: 2879624.6
      num_examples: 2700
    - name: motion_blur
      num_bytes: 2881150.1
      num_examples: 2700
    - name: pixelate
      num_bytes: 2114682.2
      num_examples: 2700
    - name: spatter
      num_bytes: 4174215.2
      num_examples: 2700
  download_size: 98645265
  dataset_size: 102804655
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: contrast
        path: data/contrast-*
      - split: gaussian_noise
        path: data/gaussian_noise-*
      - split: impulse_noise
        path: data/impulse_noise-*
      - split: jpeg_compression
        path: data/jpeg_compression-*
      - split: motion_blur
        path: data/motion_blur-*
      - split: pixelate
        path: data/pixelate-*
      - split: spatter
        path: data/spatter-*

Dataset Card for EuroSAT

Dataset Source

Paper with code

Usage

from datasets import load_dataset

dataset = load_dataset('tranganke/eurosat')

Data Fields

The dataset contains the following fields:

image: An image in RGB format.
label: The label for the image, which is one of 10 classes:
- 0: annual crop land
- 1: forest
- 2: brushland or shrubland
- 3: highway or road
- 4: industrial buildings or commercial buildings
- 5: pasture land
- 6: permanent crop land
- 7: residential buildings or homes or apartments
- 8: river
- 9: lake or sea

Data Splits

The dataset contains the following splits:

train: 21,600 examples
test: 2,700 examples
contrast: 2,700 examples
gaussian_noise: 2,700 example
impulse_noise: 2,700 examples
jpeg_compression: 2,700 examples
motion_blur: 2,700 examples
pixelate: 2,700 examples
spatter: 2,700 examples

You can use any of the provided BibTeX entries for your reference list:

@article{helber2019eurosat,
  title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
  author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  volume={12},
  number={7},
  pages={2217--2226},
  year={2019},
  publisher={IEEE}
}

@misc{yangAdaMergingAdaptiveModel2023,
  title = {{{AdaMerging}}: {{Adaptive Model Merging}} for {{Multi-Task Learning}}},
  shorttitle = {{{AdaMerging}}},
  author = {Yang, Enneng and Wang, Zhenyi and Shen, Li and Liu, Shiwei and Guo, Guibing and Wang, Xingwei and Tao, Dacheng},
  year = {2023},
  month = oct,
  number = {arXiv:2310.02575},
  eprint = {2310.02575},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2310.02575},
  url = {http://arxiv.org/abs/2310.02575},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning}
}

@misc{tangConcreteSubspaceLearning2023,
  title = {Concrete {{Subspace Learning}} Based {{Interference Elimination}} for {{Multi-task Model Fusion}}},
  author = {Tang, Anke and Shen, Li and Luo, Yong and Ding, Liang and Hu, Han and Du, Bo and Tao, Dacheng},
  year = {2023},
  month = dec,
  number = {arXiv:2312.06173},
  eprint = {2312.06173},
  publisher = {arXiv},
  url = {http://arxiv.org/abs/2312.06173},
  archiveprefix = {arxiv},
  copyright = {All rights reserved},
  keywords = {Computer Science - Machine Learning}
}


@misc{tangMergingMultiTaskModels2024,
  title = {Merging {{Multi-Task Models}} via {{Weight-Ensembling Mixture}} of {{Experts}}},
  author = {Tang, Anke and Shen, Li and Luo, Yong and Yin, Nan and Zhang, Lefei and Tao, Dacheng},
  year = {2024},
  month = feb,
  number = {arXiv:2402.00433},
  eprint = {2402.00433},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2402.00433},
  url = {http://arxiv.org/abs/2402.00433},
  archiveprefix = {arxiv},
  copyright = {All rights reserved},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning}
}