- Description:
SA-1B Download
Segment Anything 1 Billion (SA-1B) is a dataset designed for training general-purpose object segmentation models from open world images. The dataset was introduced in the paper "Segment Anything".
The SA-1B dataset consists of 11M diverse, high-resolution, licensed, and privacy-protecting images and 1.1B mask annotations. Masks are given in the COCO run-length encoding (RLE) format, and do not have classes.
The license is custom. Please, read the full terms and conditions on https://ai.facebook.com/datasets/segment-anything-downloads
All the features are in the original dataset except image.content
(content of
the image).
You can decode segmentation masks with:
import tensorflow_datasets as tfds
pycocotools = tfds.core.lazy_imports.pycocotools
ds = tfds.load('segment_anything', split='train')
for example in tfds.as_numpy(ds):
segmentation = example['annotations']['segmentation']
for counts, size in zip(segmentation['counts'], segmentation['size']):
encoded_mask = {'size': size, 'counts': counts}
mask = pycocotools.decode(encoded_mask) # np.array(dtype=uint8) mask
...
Homepage: https://ai.facebook.com/datasets/segment-anything-downloads
Source code:
tfds.datasets.segment_anything.Builder
Versions:
1.0.0
(default): Initial release.
Download size:
10.28 TiB
Dataset size:
10.59 TiB
Manual download instructions: This dataset requires you to download the source data manually into
download_config.manual_dir
(defaults to~/tensorflow_datasets/downloads/manual/
):
Download the links file from https://ai.facebook.com/datasets/segment-anything-downloadsmanual_dir
should contain the links file saved as segment_anything_links.txt.Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'train' |
11,185,362 |
- Feature structure:
FeaturesDict({
'annotations': Sequence({
'area': Scalar(shape=(), dtype=uint64, description=The area in pixels of the mask.),
'bbox': BBoxFeature(shape=(4,), dtype=float32, description=The box around the mask, in TFDS format.),
'crop_box': BBoxFeature(shape=(4,), dtype=float32, description=The crop of the image used to generate the mask, in TFDS format.),
'id': Scalar(shape=(), dtype=uint64, description=Identifier for the annotation.),
'point_coords': Tensor(shape=(1, 2), dtype=float64, description=The point coordinates input to the model to generate the mask.),
'predicted_iou': Scalar(shape=(), dtype=float64, description=The model's own prediction of the mask's quality.),
'segmentation': FeaturesDict({
'counts': string,
'size': Tensor(shape=(2,), dtype=uint64),
}),
'stability_score': Scalar(shape=(), dtype=float64, description=A measure of the mask's quality.),
}),
'image': FeaturesDict({
'content': Image(shape=(None, None, 3), dtype=uint8, description=Content of the image.),
'file_name': string,
'height': uint64,
'image_id': uint64,
'width': uint64,
}),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
annotations | Sequence | |||
annotations/area | Scalar | uint64 | The area in pixels of the mask. | |
annotations/bbox | BBoxFeature | (4,) | float32 | The box around the mask, in TFDS format. |
annotations/crop_box | BBoxFeature | (4,) | float32 | The crop of the image used to generate the mask, in TFDS format. |
annotations/id | Scalar | uint64 | Identifier for the annotation. | |
annotations/point_coords | Tensor | (1, 2) | float64 | The point coordinates input to the model to generate the mask. |
annotations/predicted_iou | Scalar | float64 | The model's own prediction of the mask's quality. | |
annotations/segmentation | FeaturesDict | Encoded segmentation mask in COCO RLE format (dict with keys size and counts ). |
||
annotations/segmentation/counts | Tensor | string | ||
annotations/segmentation/size | Tensor | (2,) | uint64 | |
annotations/stability_score | Scalar | float64 | A measure of the mask's quality. | |
image | FeaturesDict | |||
image/content | Image | (None, None, 3) | uint8 | Content of the image. |
image/file_name | Tensor | string | ||
image/height | Tensor | uint64 | ||
image/image_id | Tensor | uint64 | ||
image/width | Tensor | uint64 |
Supervised keys (See
as_supervised
doc):None
Figure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe): Missing.
Citation:
@misc{kirillov2023segment,
title={Segment Anything},
author={Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick},
year={2023},
eprint={2304.02643},
archivePrefix={arXiv},
primaryClass={cs.CV}
}