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README.md

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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
+---
+
+
+# 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}
+}
+```

data/contrast.zip

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+size 3117057

data/gaussian_noise.zip

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+size 4962476

data/impulse_noise.zip

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data/jpeg_compression.zip

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data/motion_blur.zip

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data/pixelate.zip

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+size 2503369

data/spatter.zip

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+size 4767237

data/test.zip

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+oid sha256:16a5ca861583acb87c8b5b5ceeb5dae0b4f712d865bd0368070870f5a168e873
+size 9634576

data/train.zip

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+oid sha256:ece9c02ec765c2a916f779d5777d85876258e47a0d9363af444cd6c42182f506
+size 77655061

eurosat.py

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+import datasets
+from datasets.data_files import DataFilesDict
+from datasets.packaged_modules.imagefolder.imagefolder import ImageFolder, ImageFolderConfig
+
+logger = datasets.logging.get_logger(__name__)
+
+
+class EuroSAT(ImageFolder):
+    R"""
+    EuroSAT dataset for image classification.
+    """
+
+    BUILDER_CONFIG_CLASS = ImageFolderConfig
+    BUILDER_CONFIGS = [
+        ImageFolderConfig(
+            name="default",
+            features=("images", "labels"),
+            data_files=DataFilesDict(
+                {
+                    split: f"data/{split}.zip"
+                    for split in ["train", "test"]
+                    + ["contrast", "gaussian_noise", "impulse_noise", "jpeg_compression", "motion_blur", "pixelate", "spatter"]
+                }
+            ),
+        )
+    ]
+
+    classnames = [
+        "annual crop land",
+        "forest",
+        "brushland or shrubland",
+        "highway or road",
+        "industrial buildings or commercial buildings",
+        "pasture land",
+        "permanent crop land",
+        "residential buildings or homes or apartments",
+        "river",
+        "lake or sea",
+    ]
+
+    clip_templates = [
+        lambda c: f"a centered satellite photo of {c}.",
+        lambda c: f"a centered satellite photo of a {c}.",
+        lambda c: f"a centered satellite photo of the {c}.",
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="EuroSAT dataset for image classification.",
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),
+                    "label": datasets.ClassLabel(names=self.classnames),
+                }
+            ),
+            supervised_keys=("image", "label"),
+            task_templates=[datasets.ImageClassification(image_column="image", label_column="label")],
+        )