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README.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: label
+    dtype:
+      class_label:
+        names:
+          '0': airplane
+          '1': airport
+          '2': baseball diamond
+          '3': basketball court
+          '4': beach
+          '5': bridge
+          '6': chaparral
+          '7': church
+          '8': circular farmland
+          '9': cloud
+          '10': commercial area
+          '11': dense residential
+          '12': desert
+          '13': forest
+          '14': freeway
+          '15': golf course
+          '16': ground track field
+          '17': harbor
+          '18': industrial area
+          '19': intersection
+          '20': island
+          '21': lake
+          '22': meadow
+          '23': medium residential
+          '24': mobile home park
+          '25': mountain
+          '26': overpass
+          '27': palace
+          '28': parking lot
+          '29': railway
+          '30': railway station
+          '31': rectangular farmland
+          '32': river
+          '33': roundabout
+          '34': runway
+          '35': sea ice
+          '36': ship
+          '37': snowberg
+          '38': sparse residential
+          '39': stadium
+          '40': storage tank
+          '41': tennis court
+          '42': terrace
+          '43': thermal power station
+          '44': wetland
+  splits:
+  - name: train
+    num_bytes: 2383019
+    num_examples: 18900
+  - name: test
+    num_bytes: 781557
+    num_examples: 6300
+  - name: contrast
+    num_bytes: 831957
+    num_examples: 6300
+  - name: gaussian_noise
+    num_bytes: 907557
+    num_examples: 6300
+  - name: impulse_noise
+    num_bytes: 894957
+    num_examples: 6300
+  - name: jpeg_compression
+    num_bytes: 932757
+    num_examples: 6300
+  - name: motion_blur
+    num_bytes: 869757
+    num_examples: 6300
+  - name: pixelate
+    num_bytes: 831957
+    num_examples: 6300
+  - name: spatter
+    num_bytes: 819357
+    num_examples: 6300
+  download_size: 916981012
+  dataset_size: 9252875
+---
+
+# [RESISC45](https://www.tensorflow.org/datasets/catalog/resisc45)
+
+## Overview
+
+The **RESISC45 Classification Model** is a deep learning-based image classification model designed to identify and classify different types of scenes from satellite imagery. This dataset contains 45 classes of scenes, with each class representing a different type of land cover or land use. 
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset('tanganke/resisc45')
+```
+
+## Dataset Information
+
+The dataset is divided into the following splits:
+
+- **Training set**: Contains 18,900 examples, used for model training.
+- **Test set**: Contains 6,300 examples, used for model evaluation and benchmarking.
+
+The dataset also includes the following augmented sets, which can be used for testing the model's robustness to various types of image corruptions:
+
+- **Contrast-enhanced set**: Contains 6,300 examples with enhanced contrast for improved feature visibility.
+- **Gaussian noise set**: Contains 6,300 examples where images have been corrupted with Gaussian noise.
+- **Impulse noise set**: Contains 6,300 examples with impulse noise.
+- **JPEG compression set**: Contains 6,300 examples where images have been compressed using JPEG encoding.
+- **Motion blur set**: Contains 6,300 examples with motion blur applied.
+- **Pixelate set**: Contains 6,300 examples where images have been pixelated.
+- **Spatter set**: Contains 6,300 examples with spatter noise.
+

data/contrast.zip

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data/gaussian_noise.zip

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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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data/spatter.zip

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+size 108574042

data/test.zip

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data/train.zip

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resisc45.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 RESISC45(ImageFolder):
+    R"""
+    RESISC45 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 = [
+        "airplane",
+        "airport",
+        "baseball diamond",
+        "basketball court",
+        "beach",
+        "bridge",
+        "chaparral",
+        "church",
+        "circular farmland",
+        "cloud",
+        "commercial area",
+        "dense residential",
+        "desert",
+        "forest",
+        "freeway",
+        "golf course",
+        "ground track field",
+        "harbor",
+        "industrial area",
+        "intersection",
+        "island",
+        "lake",
+        "meadow",
+        "medium residential",
+        "mobile home park",
+        "mountain",
+        "overpass",
+        "palace",
+        "parking lot",
+        "railway",
+        "railway station",
+        "rectangular farmland",
+        "river",
+        "roundabout",
+        "runway",
+        "sea ice",
+        "ship",
+        "snowberg",
+        "sparse residential",
+        "stadium",
+        "storage tank",
+        "tennis court",
+        "terrace",
+        "thermal power station",
+        "wetland",
+    ]
+
+    clip_templates = [
+        lambda c: f"satellite imagery of {c}.",
+        lambda c: f"aerial imagery of {c}.",
+        lambda c: f"satellite photo of {c}.",
+        lambda c: f"aerial photo of {c}.",
+        lambda c: f"satellite view of {c}.",
+        lambda c: f"aerial view of {c}.",
+        lambda c: f"satellite imagery of a {c}.",
+        lambda c: f"aerial imagery of a {c}.",
+        lambda c: f"satellite photo of a {c}.",
+        lambda c: f"aerial photo of a {c}.",
+        lambda c: f"satellite view of a {c}.",
+        lambda c: f"aerial view of a {c}.",
+        lambda c: f"satellite imagery of the {c}.",
+        lambda c: f"aerial imagery of the {c}.",
+        lambda c: f"satellite photo of the {c}.",
+        lambda c: f"aerial photo of the {c}.",
+        lambda c: f"satellite view of the {c}.",
+        lambda c: f"aerial view of the {c}.",
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description="RESISC45 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")],
+        )