yolox
pixeltable.ext.functions.yolox
yolo_to_coco
yolo_to_coco(detections: JsonT) -> JsonT
Converts the output of a YOLOX object detection model to COCO format.
YOLOX is part of the pixeltable.ext
package: long-term support in Pixeltable is not guaranteed.
Parameters:
-
detections
(JsonT
) –The output of a YOLOX object detection model, as returned by
yolox
.
Returns:
-
JsonT
–A dictionary containing the data from
detections
, converted to COCO format.
Examples:
Add a computed column that converts the output tbl.detections
to COCO format, where tbl.image
is the image for which detections were computed:
>>> tbl['detections'] = yolox(tbl.image, model_id='yolox_m', threshold=0.8)
... tbl['detections_coco'] = yolo_to_coco(tbl.detections)
yolox
yolox(images: ImageT, *, model_id: str, threshold: float = 0.5) -> JsonT
Computes YOLOX object detections for the specified image. model_id
should reference one of the models
defined in the YOLOX documentation.
YOLOX is part of the pixeltable.ext
package: long-term support in Pixeltable is not guaranteed.
Requirements:
pip install git+https://github.com/Megvii-BaseDetection/YOLOX
Parameters:
-
model_id
(str
) –one of:
yolox_nano
,yolox_tiny
,yolox_s
,yolox_m
,yolox_l
,yolox_x
-
threshold
(float
, default:0.5
) –the threshold for object detection
Returns:
-
JsonT
–A dictionary containing the output of the object detection model.
Examples:
Add a computed column that applies the model yolox_m
to an existing
Pixeltable column tbl.image
of the table tbl
:
>>> tbl['detections'] = yolox(tbl.image, model_id='yolox_m', threshold=0.8)