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体验新版 GitCode,发现更多精彩内容 >>
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aed6de5c
编写于
9月 09, 2022
作者:
A
Anastasia Yasakova
提交者:
GitHub
9月 09, 2022
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Update COCO documentation (#4908)
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CHANGELOG.md
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site/content/en/docs/manual/advanced/formats/format-coco.md
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CHANGELOG.md
浏览文件 @
aed6de5c
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@@ -26,7 +26,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
Skeleton (
<https://github.com/cvat-ai/cvat/pull/1>
), (
<https://github.com/opencv/cvat/pull/4829>
)
-
Added helm chart support for serverless functions and analytics (
<https://github.com/cvat-ai/cvat/pull/110>
)
-
Added confirmation when remove a track (
<https://github.com/opencv/cvat/pull/4846>
)
-
[
COCO Keypoints
](
https://cocodataset.org/#keypoints-2020
)
format support (
<https://github.com/opencv/cvat/pull/4821>
)
-
[
COCO Keypoints
](
https://cocodataset.org/#keypoints-2020
)
format support (
<https://github.com/opencv/cvat/pull/4821>
,
<https://github.com/opencv/cvat/pull/4908>
)
-
Support for Oracle OCI Buckets (
<https://github.com/opencv/cvat/pull/4876>
)
-
`cvat-sdk`
and
`cvat-cli`
packages on PyPI (
<https://github.com/opencv/cvat/pull/4903>
)
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README.md
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@@ -125,6 +125,7 @@ For more information about the supported formats, look at the
| Segmentation masks from
[
PASCAL VOC
](
http://host.robots.ox.ac.uk/pascal/VOC/
)
| ✔️ | ✔️ |
|
[
YOLO
](
https://pjreddie.com/darknet/yolo/
)
| ✔️ | ✔️ |
|
[
MS COCO Object Detection
](
http://cocodataset.org/#format-data
)
| ✔️ | ✔️ |
|
[
MS COCO Keypoints Detection
](
http://cocodataset.org/#format-data
)
| ✔️ | ✔️ |
|
[
TFrecord
](
https://www.tensorflow.org/tutorials/load_data/tfrecord
)
| ✔️ | ✔️ |
|
[
MOT
](
https://motchallenge.net/
)
| ✔️ | ✔️ |
|
[
LabelMe 3.0
](
http://labelme.csail.mit.edu/Release3.0
)
| ✔️ | ✔️ |
...
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site/content/en/docs/manual/advanced/formats/format-coco.md
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aed6de5c
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@@ -41,6 +41,32 @@ Uploaded file: a single unpacked `*.json` or a zip archive with the structure de
-
supported annotations: Polygons, Rectangles (if the
`segmentation`
field is empty)
# [MS COCO Keypoint Detection](https://cocodataset.org/#keypoints-2020)
-
[
Format specification
](
https://openvinotoolkit.github.io/datumaro/docs/formats/coco/
)
## COCO export
Downloaded file: a zip archive with the structure described
[
here
](
https://openvinotoolkit.github.io/datumaro/docs/formats/coco/#import-coco-dataset
)
-
supported annotations: Skeletons
-
supported attributes:
-
`is_crowd`
(checkbox or integer with values 0 and 1) -
specifies that the instance (an object group) should have an
RLE-encoded mask in the
`segmentation`
field. All the grouped shapes
are merged into a single mask, the largest one defines all
the object properties
-
`score`
(number) - the annotation
`score`
field
-
arbitrary attributes - will be stored in the
`attributes`
annotation section
## COCO import
Uploaded file: a single unpacked
`*.json`
or a zip archive with the structure described
[
here
](
https://openvinotoolkit.github.io/datumaro/docs/formats/coco/#import-coco-dataset
)
(without images).
-
supported annotations: Skeletons
## How to create a task from MS COCO dataset
1.
Download the
[
MS COCO dataset
](
https://openvinotoolkit.github.io/datumaro/docs/formats/coco/#import-coco-dataset
)
.
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