Comments (3)
You can read the YOLOv5 predictions with AnnotationSet.from_yolov5()
and and then convert them to COCO using the .save_coco()
method:
from globox import AnnotationSet
predictions = AnnotationSet.from_yolo_v5("path/to/annotations", image_folder="path/to/images")
predictions.save_coco(
"coco_preds.json",
label_to_id={"person": 0},
imageid_to_id={im: i for i, im in enumerate(sorted(predictions.image_ids))}
)
Conversion to COCO is a little bit tricky as you can see because COCO uses integer ids rather and image names and str class names. In any case you should adapt label_to_id
and imageid_to_id
to match the id mapping from the DETR dataset, I'm just suggesting an example.
Note that you can also read COCO annotations with AnnotationSet.from_coco()
, which could simplify your workflow.
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Related Issues (20)
- An error occurred while converting from Yolo format to coco format. HOT 1
- NameError: name 'AnnotationSet' is not defined HOT 4
- Unable to solve the missed image problem HOT 2
- TypeError: unsupported operand type(s) for +: 'AnnotationSet' and 'AnnotationSet' HOT 3
- Infinite recursion in `BoundingBox.from_yolo_v7()` HOT 1
- Assertion Error HOT 2
- Single Image score HOT 1
- Missing area in COCO's .json output? HOT 5
- Segment key remains blank HOT 1
- globox summary: error: the following arguments are required: input HOT 8
- repo download takes a long time
- repo download takes a long time HOT 2
- Relax globox numpy version requirements so it can coexist with tensorflow HOT 1
- Yolo txt To coco json Conversion HOT 5
- YoloV5 label file parsing bug HOT 2
- Import HOT 7
- TypeError: 'type' object is not subscriptable HOT 1
- Import error HOT 2
- Converting from YOLO annotation files to a format readable by the VGG Image Annotator (VIA) HOT 11
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