A widely-used machine learning structure, the COCO dataset is instrumental for tasks involving object identification and image segmentation. This format is compatible with projects that employ bounding boxes or polygonal image annotations.
For more information, see:
For export of images and videos:
- Supported annotations: Bounding Boxes, Polygons.
is_crowdThis can either be a checkbox or an integer (with values of 0 or 1). It indicates that the instance (or group of objects) should include an RLE-encoded mask in the
segmentationfield. All shapes within the group coalesce into a single, overarching mask, with the largest shape setting the properties for the entire object group.
score: This numerical field represents the annotation
- Arbitrary attributes: These will be stored within the
attributessection of the annotation.
- Tracks: Not supported.
The downloaded file is a .zip archive with the following structure:
archive.zip/ ├── images/ │ ├── train/ │ │ ├── <image_name1.ext> │ │ ├── <image_name2.ext> │ │ └── ... │ └── val/ │ ├── <image_name1.ext> │ ├── <image_name2.ext> │ └── ... └── annotations/ ├── <task>_<subset_name>.json └── ...
When exporting a dataset from a Project, subset names will mirror those used within the project itself.
Otherwise, a singular default subset will be created to house all the dataset information.
Uplod format: a single unpacked
*.json or a zip archive with the structure described above or
- supported annotations: Polygons, Rectangles (if the
segmentationfield is empty)
- supported tasks:
person_keypoints(only segmentations will be imported),
How to create a task from MS COCO dataset
Download the MS COCO dataset.
Create a CVAT task with the following labels:
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
val2017.zipas data (See Creating an annotation task guide for details)
Upload annotationbutton, choose
COCO 1.1and select
instances_val2017.jsonannotation file. It can take some time.