@ -5,21 +5,11 @@ weight: 5
### [MS COCO Object Detection ](http://cocodataset.org/#format-data )< a id = "coco" />
### [MS COCO Object Detection ](http://cocodataset.org/#format-data )< a id = "coco" />
- [Format specification ](http ://cocodataset.org/#format-data )
- [Format specification ](http s://github.com/openvinotoolkit/datumaro/blob/develop/docs/formats/coco_user_manual.md#format-specification )
#### COCO export
#### COCO export
Downloaded file: a zip archive with following structure:
Downloaded file: a zip archive with the structure described [here ](https://github.com/openvinotoolkit/datumaro/blob/develop/docs/formats/coco_user_manual.md#load-coco-dataset )
```bash
archive.zip/
├── images/
│ ├── < image_name1.ext >
│ ├── < image_name2.ext >
│ └── ...
└── annotations/
└── instances_default.json
```
- supported annotations: Polygons, Rectangles
- supported annotations: Polygons, Rectangles
- supported attributes:
- supported attributes:
@ -31,7 +21,8 @@ archive.zip/
- `score` (number) - the annotation `score` field
- `score` (number) - the annotation `score` field
- arbitrary attributes - will be stored in the `attributes` annotation section
- arbitrary attributes - will be stored in the `attributes` annotation section
_Note_: there is also a [support for COCO keypoints over Datumaro ](https://github.com/openvinotoolkit/cvat/issues/2910#issuecomment-726077582 )
Support for COCO tasks via Datumaro is described [here ](https://github.com/openvinotoolkit/datumaro/blob/develop/docs/formats/coco_user_manual.md#export-to-coco )
For example, [support for COCO keypoints over Datumaro ](https://github.com/openvinotoolkit/cvat/issues/2910#issuecomment-726077582 ):
1. Install [Datumaro ](https://github.com/openvinotoolkit/datumaro )
1. Install [Datumaro ](https://github.com/openvinotoolkit/datumaro )
`pip install datumaro`
`pip install datumaro`
@ -44,16 +35,17 @@ keypoint lists (without the `visibility` COCO flag).
#### COCO import
#### COCO import
Uploaded file: a single unpacked `*.json` or a zip archive with the structure above (without images).
Uploaded file: a single unpacked `*.json` or a zip archive with the structure described
[here ](https://github.com/openvinotoolkit/datumaro/blob/develop/docs/formats/coco_user_manual.md#load-coco-dataset )
(without images).
- supported annotations: Polygons, Rectangles (if the `segmentation` field is empty)
- supported annotations: Polygons, Rectangles (if the `segmentation` field is empty)
#### How to create a task from MS COCO dataset
#### How to create a task from MS COCO dataset
1. Download the [MS COCO dataset ](http ://cocodataset.org/#download ).
1. Download the [MS COCO dataset ](http s://github.com/openvinotoolkit/datumaro/blob/develop/docs/formats/coco_user_manual.md#load-COCO-dataset ).
For example [2017 Val images ](http://images.cocodataset.org/zips/val2017.zip )
For example `val images` and `instances` annotations
and [2017 Train/Val annotations ](http://images.cocodataset.org/annotations/annotations_trainval2017.zip ).
1. Create a CVAT task with the following labels:
1. Create a CVAT task with the following labels:
@ -61,12 +53,12 @@ Uploaded file: a single unpacked `*.json` or a zip archive with the structure ab
person bicycle car motorcycle airplane bus train truck boat "traffic light" "fire hydrant" "stop sign" "parking meter" bench bird cat dog horse sheep cow elephant bear zebra giraffe backpack umbrella handbag tie suitcase frisbee skis snowboard "sports ball" kite "baseball bat" "baseball glove" skateboard surfboard "tennis racket" bottle "wine glass" cup fork knife spoon bowl banana apple sandwich orange broccoli carrot "hot dog" pizza donut cake chair couch "potted plant" bed "dining table" toilet tv laptop mouse remote keyboard "cell phone" microwave oven toaster sink refrigerator book clock vase scissors "teddy bear" "hair drier" toothbrush
person bicycle car motorcycle airplane bus train truck boat "traffic light" "fire hydrant" "stop sign" "parking meter" bench bird cat dog horse sheep cow elephant bear zebra giraffe backpack umbrella handbag tie suitcase frisbee skis snowboard "sports ball" kite "baseball bat" "baseball glove" skateboard surfboard "tennis racket" bottle "wine glass" cup fork knife spoon bowl banana apple sandwich orange broccoli carrot "hot dog" pizza donut cake chair couch "potted plant" bed "dining table" toilet tv laptop mouse remote keyboard "cell phone" microwave oven toaster sink refrigerator book clock vase scissors "teddy bear" "hair drier" toothbrush
```
```
1. Select val2017.zip as data
1. Select ` val2017.zip` as data
(See [Creating an annotation task ](/docs/for-users/user-guide/creating_an_annotation_task/ )
(See [Creating an annotation task ](/docs/for-users/user-guide/creating_an_annotation_task/ )
guide for details)
guide for details)
1. Unpack `annotations_trainval2017.zip`
1. Unpack `annotations_trainval2017.zip`
1. click `Upload annotation` button,
1. click `Upload annotation` button,
choose `COCO 1.1` and select `instances_val2017.json .json `
choose `COCO 1.1` and select `instances_val2017.json `
annotation file. It can take some time.
annotation file. It can take some time.