Datasets:
BSD100

Task Categories: other
Multilinguality: monolingual
Size Categories: unknown
Language Creators: found
Annotations Creators: machine-generated
Source Datasets: original
BSD100 / README.md
Eugene Siow
Add data. 600666e
1 ---
2 annotations_creators:
3 - machine-generated
4 language_creators:
5 - found
6 languages: []
7 licenses:
8 - other-academic-use
9 multilinguality:
10 - monolingual
11 pretty_name: BSD100
12 size_categories:
13 - unknown
14 source_datasets:
15 - original
16 task_categories:
17 - other
18 task_ids:
19 - other-other-image-super-resolution
20 ---
21
22 # Dataset Card for BSD100
23
24 ## Table of Contents
25 - [Table of Contents](#table-of-contents)
26 - [Dataset Description](#dataset-description)
27 - [Dataset Summary](#dataset-summary)
28 - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
29 - [Languages](#languages)
30 - [Dataset Structure](#dataset-structure)
31 - [Data Instances](#data-instances)
32 - [Data Fields](#data-fields)
33 - [Data Splits](#data-splits)
34 - [Dataset Creation](#dataset-creation)
35 - [Curation Rationale](#curation-rationale)
36 - [Source Data](#source-data)
37 - [Annotations](#annotations)
38 - [Personal and Sensitive Information](#personal-and-sensitive-information)
39 - [Considerations for Using the Data](#considerations-for-using-the-data)
40 - [Social Impact of Dataset](#social-impact-of-dataset)
41 - [Discussion of Biases](#discussion-of-biases)
42 - [Other Known Limitations](#other-known-limitations)
43 - [Additional Information](#additional-information)
44 - [Dataset Curators](#dataset-curators)
45 - [Licensing Information](#licensing-information)
46 - [Citation Information](#citation-information)
47 - [Contributions](#contributions)
48
49 ## Dataset Description
50
51 - **Homepage**: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/bsds/
52 - **Repository**: https://huggingface.co/datasets/eugenesiow/BSD100
53 - **Paper**: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655
54 - **Leaderboard**: https://github.com/eugenesiow/super-image#scale-x2
55
56 ### Dataset Summary
57
58 BSD is a dataset used frequently for image denoising and super-resolution. Of the subdatasets, BSD100 is aclassical image dataset having 100 test images proposed by [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655). The dataset is composed of a large variety of images ranging from natural images to object-specific such as plants, people, food etc. BSD100 is the testing set of the Berkeley segmentation dataset BSD300.
59
60 Install with `pip`:
61 ```bash
62 pip install datasets super-image
63 ```
64
65 Evaluate a model with the [`super-image`](https://github.com/eugenesiow/super-image) library:
66 ```python
67 from datasets import load_dataset
68 from super_image import EdsrModel
69 from super_image.data import EvalDataset, EvalMetrics
70
71 dataset = load_dataset('eugenesiow/BSD100', 'bicubic_x2', split='validation')
72 eval_dataset = EvalDataset(dataset)
73 model = EdsrModel.from_pretrained('eugenesiow/edsr-base', scale=2)
74 EvalMetrics().evaluate(model, eval_dataset)
75 ```
76
77 ### Supported Tasks and Leaderboards
78
79 The dataset is commonly used for evaluation of the `image-super-resolution` task.
80
81 Unofficial [`super-image`](https://github.com/eugenesiow/super-image) leaderboard for:
82 - [Scale 2](https://github.com/eugenesiow/super-image#scale-x2)
83 - [Scale 3](https://github.com/eugenesiow/super-image#scale-x3)
84 - [Scale 4](https://github.com/eugenesiow/super-image#scale-x4)
85 - [Scale 8](https://github.com/eugenesiow/super-image#scale-x8)
86
87 ### Languages
88
89 Not applicable.
90
91 ## Dataset Structure
92
93 ### Data Instances
94
95 An example of `validation` for `bicubic_x2` looks as follows.
96 ```
97 {
98 "hr": "/.cache/huggingface/datasets/downloads/extracted/BSD100_HR/3096.png",
99 "lr": "/.cache/huggingface/datasets/downloads/extracted/BSD100_LR_x2/3096.png"
100 }
101 ```
102
103 ### Data Fields
104
105 The data fields are the same among all splits.
106
107 - `hr`: a `string` to the path of the High Resolution (HR) `.png` image.
108 - `lr`: a `string` to the path of the Low Resolution (LR) `.png` image.
109
110 ### Data Splits
111
112 | name |validation|
113 |-------|---:|
114 |bicubic_x2|100|
115 |bicubic_x3|100|
116 |bicubic_x4|100|
117
118
119 ## Dataset Creation
120
121 ### Curation Rationale
122
123 [More Information Needed]
124
125 ### Source Data
126
127 #### Initial Data Collection and Normalization
128
129 [More Information Needed]
130
131 #### Who are the source language producers?
132
133 [More Information Needed]
134
135 ### Annotations
136
137 #### Annotation process
138
139 No annotations.
140
141 #### Who are the annotators?
142
143 No annotators.
144
145 ### Personal and Sensitive Information
146
147 [More Information Needed]
148
149 ## Considerations for Using the Data
150
151 ### Social Impact of Dataset
152
153 [More Information Needed]
154
155 ### Discussion of Biases
156
157 [More Information Needed]
158
159 ### Other Known Limitations
160
161 [More Information Needed]
162
163 ## Additional Information
164
165 ### Dataset Curators
166
167 - **Original Authors**: [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655)
168
169 ### Licensing Information
170
171 You are free to download a portion of the dataset for non-commercial research and educational purposes.
172 In exchange, we request only that you make available to us the results of running your segmentation or
173 boundary detection algorithm on the test set as described below. Work based on the dataset should cite
174 the [Martin et al. (2001)](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=937655) paper.
175
176 ### Citation Information
177
178 ```bibtex
179 @inproceedings{martin2001database,
180 title={A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics},
181 author={Martin, David and Fowlkes, Charless and Tal, Doron and Malik, Jitendra},
182 booktitle={Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001},
183 volume={2},
184 pages={416--423},
185 year={2001},
186 organization={IEEE}
187 }
188 ```
189
190 ### Contributions
191
192 Thanks to [@eugenesiow](https://github.com/eugenesiow) for adding this dataset.
193