module pixeltable.functions.yolox

YOLOX object detection functions.

udf yolo_to_coco()

yolo_to_coco(detections: Json) -> Json

Converts the output of a YOLOX object detection model to COCO format. Parameters:

detections (Json): The output of a YOLOX object detection model, as returned by yolox.

Returns:

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))

udf yolox()

yolox(
    images: Image,
    *,
    model_id: String,
    threshold: Float = 0.5
) -> 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 (String): one of: yolox_nano, yolox_tiny, yolox_s, yolox_m, yolox_l, yolox_x
threshold (Float): the threshold for object detection

Returns:

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)
)

​module pixeltable.functions.yolox

​udf yolo_to_coco()

​udf yolox()

module pixeltable.functions.yolox

udf yolo_to_coco()

udf yolox()