Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
File size: 4,488 Bytes
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---
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: 3962928
num_examples: 21600
- name: test
num_bytes: 489995
num_examples: 2700
- name: contrast
num_bytes: 511595
num_examples: 2700
- name: gaussian_noise
num_bytes: 543995
num_examples: 2700
- name: impulse_noise
num_bytes: 538595
num_examples: 2700
- name: jpeg_compression
num_bytes: 554795
num_examples: 2700
- name: motion_blur
num_bytes: 527795
num_examples: 2700
- name: pixelate
num_bytes: 511595
num_examples: 2700
- name: spatter
num_bytes: 506195
num_examples: 2700
download_size: 114799801
dataset_size: 8147488
task_categories:
- image-classification
language:
- en
size_categories:
- 10K<n<100K
---
# Dataset Card for EuroSAT
# Dataset Source
- [Paper with code](https://paperswithcode.com/dataset/eurosat)
# Usage
```python
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:
```bibtex
@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}
}
``` |