Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
10K - 100K
License:
File size: 3,617 Bytes
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---
annotations_creators: []
language: en
license: unknown
size_categories:
- 10K<n<100K
task_categories:
- image-segmentation
task_ids: []
pretty_name: DUTS
tags:
- fiftyone
- image
- image-segmentation
exists_ok: true
dataset_summary: '
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 15572 samples.
## Installation
If you haven''t already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include ''max_samples'', etc
dataset = fouh.load_from_hub("Voxel51/DUTS")
# Launch the App
session = fo.launch_app(dataset)
```
'
---
# Dataset Card for DUTS
<!-- Provide a quick summary of the dataset. -->
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 15572 samples.
## Installation
If you haven't already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/DUTS")
# Launch the App
session = fo.launch_app(dataset)
```
## Dataset Details
### Dataset Description
DUTS is a saliency detection dataset containing 10,553 training images and 5,019 test images. All training images are collected from the ImageNet DET training/val sets, while test images are collected from the ImageNet DET test set and the SUN data set. Both the training and test set contain very challenging scenarios for saliency detection. Accurate pixel-level ground truths are manually annotated by 50 subjects.
- **Curated by:** Lijun Wang, Huchuan Lu, Yifan Wang, Mengyang Feng, Dong Wang, Baocai Yin, and Xiang Ruan
- **Language(s) (NLP):** en
- **License:** unknown
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
```
Name: DUTS
Media type: image
Num samples: 15572
Persistent: False
Tags: []
Sample fields:
id: fiftyone.core.fields.ObjectIdField
filepath: fiftyone.core.fields.StringField
tags: fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
metadata: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)
ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Segmentation)
```
The dataset has 2 splits: "train" and "test". Samples are tagged with their split.
## Dataset Creation
Introduced by Wang et al. in [Learning to Detect Salient Objects With Image-Level Supervision](https://paperswithcode.com/paper/learning-to-detect-salient-objects-with-image)
## Citation
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@inproceedings{wang2017,
title={Learning to Detect Salient Objects with Image-level Supervision},
author={Wang, Lijun and Lu, Huchuan and Wang, Yifan and Feng, Mengyang
and Wang, Dong, and Yin, Baocai and Ruan, Xiang},
booktitle={CVPR},
year={2017}
}
```
## Dataset Card Authors
Dataset conversion and data card contributed by [Rohith Raj Srinivasan](https://huggingface.co/rohis).
## Dataset Card Contact
[Rohith Raj Srinivasan](https://huggingface.co/rohis) |