File size: 3,713 Bytes
e7adcd2 17e8228 e7adcd2 17e8228 e7adcd2 17e8228 e7adcd2 17e8228 e7adcd2 17e8228 e7adcd2 17e8228 e7adcd2 17e8228 e7adcd2 17e8228 e7adcd2 17e8228 e7adcd2 17e8228 e7adcd2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
---
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
## 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.
|