MS COCO Object Detection
COCO export
Downloaded file: a zip archive with the structure described here
- supported annotations: Polygons, Rectangles
- 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 thesegmentation
field. All the grouped shapes are merged into a single mask, the largest one defines all the object propertiesscore
(number) - the annotationscore
field- arbitrary attributes - will be stored in the
attributes
annotation section
Support for COCO tasks via Datumaro is described here For example, support for COCO keypoints over Datumaro:
- Install Datumaro
pip install datumaro
- Export the task in the
Datumaro
format, unzip - Export the Datumaro project in
coco
/coco_person_keypoints
formatsdatum export -f coco -p path/to/project [-- --save-images]
This way, one can export CVAT points as single keypoints or
keypoint lists (without the visibility
COCO flag).
COCO import
Uploaded file: a single unpacked *.json
or a zip archive with the structure described
here
(without images).
- supported annotations: Polygons, Rectangles (if the
segmentation
field is empty)
How to create a task from MS COCO dataset
-
Download the MS COCO dataset.
For example
val images
andinstances
annotations -
Create a CVAT task with the following labels:
-
Select
val2017.zip
as data (See Creating an annotation task guide for details) -
Unpack
annotations_trainval2017.zip
-
click
Upload annotation
button, chooseCOCO 1.1
and selectinstances_val2017.json
annotation file. It can take some time.