|
--- |
|
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: 246710368.7 |
|
num_examples: 18900 |
|
- name: test |
|
num_bytes: 87460774.8 |
|
num_examples: 6300 |
|
- name: contrast |
|
num_bytes: 67512032.7 |
|
num_examples: 6300 |
|
- name: gaussian_noise |
|
num_bytes: 116440617.3 |
|
num_examples: 6300 |
|
- name: impulse_noise |
|
num_bytes: 125449913.4 |
|
num_examples: 6300 |
|
- name: jpeg_compression |
|
num_bytes: 85196403.6 |
|
num_examples: 6300 |
|
- name: motion_blur |
|
num_bytes: 73908158.1 |
|
num_examples: 6300 |
|
- name: pixelate |
|
num_bytes: 5573022.0 |
|
num_examples: 6300 |
|
- name: spatter |
|
num_bytes: 109007915.1 |
|
num_examples: 6300 |
|
download_size: 911199338 |
|
dataset_size: 917259205.7 |
|
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-* |
|
--- |
|
|
|
# [RESISC45](https://www.tensorflow.org/datasets/catalog/resisc45) |
|
|
|
## Overview |
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|
|
## Usage |
|
|
|
```python |
|
from datasets import load_dataset |
|
|
|
# Load the dataset |
|
dataset = load_dataset('tanganke/resisc45') |
|
``` |
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|
|
## 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. |
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|
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|
