scene_parse150
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Scene parsing is to segment and parse an image into different image regions
associated with semantic categories, such as sky, road, person, and bed. MIT
Scene Parsing Benchmark (SceneParse150) provides a standard training and
evaluation platform for the algorithms of scene parsing.
Split |
Examples |
'test' |
2,000 |
'train' |
20,210 |
FeaturesDict({
'annotation': Image(shape=(None, None, 3), dtype=uint8),
'image': Image(shape=(None, None, 3), dtype=uint8),
})
Feature |
Class |
Shape |
Dtype |
Description |
|
FeaturesDict |
|
|
|
annotation |
Image |
(None, None, 3) |
uint8 |
|
image |
Image |
(None, None, 3) |
uint8 |
|
@inproceedings{zhou2017scene,
title={Scene Parsing through ADE20K Dataset},
author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
year={2017}
}
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Last updated 2024-06-01 UTC.
[null,null,["Last updated 2024-06-01 UTC."],[],[],null,["# scene_parse150\n\n\u003cbr /\u003e\n\n- **Description**:\n\nScene parsing is to segment and parse an image into different image regions\nassociated with semantic categories, such as sky, road, person, and bed. MIT\nScene Parsing Benchmark (SceneParse150) provides a standard training and\nevaluation platform for the algorithms of scene parsing.\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/ade20k)\n\n- **Homepage** :\n \u003chttp://sceneparsing.csail.mit.edu/\u003e\n\n- **Source code** :\n [`tfds.datasets.scene_parse150.Builder`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/scene_parse150/scene_parse150_dataset_builder.py)\n\n- **Versions**:\n\n - **`1.0.0`** (default): No release notes.\n- **Download size** : `936.97 MiB`\n\n- **Dataset size** : `904.91 MiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n No\n\n- **Splits**:\n\n| Split | Examples |\n|-----------|----------|\n| `'test'` | 2,000 |\n| `'train'` | 20,210 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'annotation': Image(shape=(None, None, 3), dtype=uint8),\n 'image': Image(shape=(None, None, 3), dtype=uint8),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|------------|--------------|-----------------|-------|-------------|\n| | FeaturesDict | | | |\n| annotation | Image | (None, None, 3) | uint8 | |\n| image | Image | (None, None, 3) | uint8 | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `('image', 'annotation')`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @inproceedings{zhou2017scene,\n title={Scene Parsing through ADE20K Dataset},\n author={Zhou, Bolei and Zhao, Hang and Puig, Xavier and Fidler, Sanja and Barriuso, Adela and Torralba, Antonio},\n booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\n year={2017}\n }"]]