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module  pixeltable.functions.yolox

YOLOX object detection functions.

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udf  yolo_to_coco()

Signature
yolo_to_coco(detections: pxt.Json) -> pxt.Json
Converts the output of a YOLOX object detection model to COCO format. Parameters:
  • detections (pxt.Json): The output of a YOLOX object detection model, as returned by yolox.
Returns:
  • pxt.Json: 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.add_computed_column(
    detections=yolox(tbl.image, model_id='yolox_m', threshold=0.8)
)
tbl.add_computed_column(detections_coco=yolo_to_coco(tbl.detections))

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udf  yolox()

Signature
yolox(
    images: pxt.Image,
    *,
    model_id: pxt.String,
    threshold: pxt.Float = 0.5
) -> pxt.Json
Computes YOLOX object detections for the specified image. model_id should reference one of the models defined in the YOLOX documentation. Requirements:
  • pip install pixeltable-yolox
Parameters:
  • model_id (pxt.String): one of: yolox_nano, yolox_tiny, yolox_s, yolox_m, yolox_l, yolox_x
  • threshold (pxt.Float): the threshold for object detection
Returns:
  • pxt.Json: 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.add_computed_column(
    detections=yolox(tbl.image, model_id='yolox_m', threshold=0.8)
)