- URL: http://cocodataset.org/#home
DatasetBuilder
:tfds.object_detection.coco.Coco
coco
is configured with tfds.object_detection.coco.CocoConfig
and has the
following configurations predefined (defaults to the first one):
2014
(v1.1.0
) (Size: 37.57 GiB
): COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2014 version. Note:- Some images from the train and validation sets don't have annotations.
- Coco 2014 and 2017 uses the same images, but different train/val/test splits
- The test split don't have any annotations (only images).
- Coco defines 91 classes but the data only uses 80 classes.
- Panotptic annotations defines defines 200 classes but only uses 133.
2017
(v1.1.0
) (Size: 25.20 GiB
): COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2017 version. Note:- Some images from the train and validation sets don't have annotations.
- Coco 2014 and 2017 uses the same images, but different train/val/test splits
- The test split don't have any annotations (only images).
- Coco defines 91 classes but the data only uses 80 classes.
- Panotptic annotations defines defines 200 classes but only uses 133.
2017_panoptic
(v1.1.0
) (Size: 19.57 GiB
): COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2017 version. Note:- Some images from the train and validation sets don't have annotations.
- Coco 2014 and 2017 uses the same images, but different train/val/test splits
- The test split don't have any annotations (only images).
- Coco defines 91 classes but the data only uses 80 classes.
- Panotptic annotations defines defines 200 classes but only uses 133.
coco/2014
COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2014 version. Note: * Some images from the train and validation sets don't have annotations. * Coco 2014 and 2017 uses the same images, but different train/val/test splits * The test split don't have any annotations (only images). * Coco defines 91 classes but the data only uses 80 classes. * Panotptic annotations defines defines 200 classes but only uses 133.
Versions:
1.1.0
(default):
Statistics
Split | Examples |
---|---|
ALL | 245,496 |
TRAIN | 82,783 |
TEST2015 | 81,434 |
TEST | 40,775 |
VALIDATION | 40,504 |
Features
FeaturesDict({
'image': Image(shape=(None, None, 3), dtype=tf.uint8),
'image/filename': Text(shape=(), dtype=tf.string),
'image/id': Tensor(shape=(), dtype=tf.int64),
'objects': Sequence({
'area': Tensor(shape=(), dtype=tf.int64),
'bbox': BBoxFeature(shape=(4,), dtype=tf.float32),
'id': Tensor(shape=(), dtype=tf.int64),
'is_crowd': Tensor(shape=(), dtype=tf.bool),
'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=80),
}),
})
Homepage
coco/2017
COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2017 version. Note: * Some images from the train and validation sets don't have annotations. * Coco 2014 and 2017 uses the same images, but different train/val/test splits * The test split don't have any annotations (only images). * Coco defines 91 classes but the data only uses 80 classes. * Panotptic annotations defines defines 200 classes but only uses 133.
Versions:
1.1.0
(default):
Statistics
Split | Examples |
---|---|
ALL | 163,957 |
TRAIN | 118,287 |
TEST | 40,670 |
VALIDATION | 5,000 |
Features
FeaturesDict({
'image': Image(shape=(None, None, 3), dtype=tf.uint8),
'image/filename': Text(shape=(), dtype=tf.string),
'image/id': Tensor(shape=(), dtype=tf.int64),
'objects': Sequence({
'area': Tensor(shape=(), dtype=tf.int64),
'bbox': BBoxFeature(shape=(4,), dtype=tf.float32),
'id': Tensor(shape=(), dtype=tf.int64),
'is_crowd': Tensor(shape=(), dtype=tf.bool),
'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=80),
}),
})
Homepage
coco/2017_panoptic
COCO is a large-scale object detection, segmentation, and captioning dataset. This version contains images, bounding boxes " and labels for the 2017 version. Note: * Some images from the train and validation sets don't have annotations. * Coco 2014 and 2017 uses the same images, but different train/val/test splits * The test split don't have any annotations (only images). * Coco defines 91 classes but the data only uses 80 classes. * Panotptic annotations defines defines 200 classes but only uses 133.
Versions:
1.1.0
(default):
Statistics
Split | Examples |
---|---|
ALL | 123,287 |
TRAIN | 118,287 |
VALIDATION | 5,000 |
Features
FeaturesDict({
'image': Image(shape=(None, None, 3), dtype=tf.uint8),
'image/filename': Text(shape=(), dtype=tf.string),
'image/id': Tensor(shape=(), dtype=tf.int64),
'panoptic_image': Image(shape=(None, None, 3), dtype=tf.uint8),
'panoptic_image/filename': Text(shape=(), dtype=tf.string),
'panoptic_objects': Sequence({
'area': Tensor(shape=(), dtype=tf.int64),
'bbox': BBoxFeature(shape=(4,), dtype=tf.float32),
'id': Tensor(shape=(), dtype=tf.int64),
'is_crowd': Tensor(shape=(), dtype=tf.bool),
'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=133),
}),
})
Homepage
Citation
@article{DBLP:journals/corr/LinMBHPRDZ14,
author = {Tsung{-}Yi Lin and
Michael Maire and
Serge J. Belongie and
Lubomir D. Bourdev and
Ross B. Girshick and
James Hays and
Pietro Perona and
Deva Ramanan and
Piotr Doll{'{a}}r and
C. Lawrence Zitnick},
title = {Microsoft {COCO:} Common Objects in Context},
journal = {CoRR},
volume = {abs/1405.0312},
year = {2014},
url = {http://arxiv.org/abs/1405.0312},
archivePrefix = {arXiv},
eprint = {1405.0312},
timestamp = {Mon, 13 Aug 2018 16:48:13 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/LinMBHPRDZ14},
bibsource = {dblp computer science bibliography, https://dblp.org}
}