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ultralytics 8.0.40 TensorRT metadata and Results visualizer (#1014)

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Glenn Jocher 2023-02-17 20:06:06 +01:00 • committed by GitHub
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10
ultralytics/yolo/utils/ops.py
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@ -139,6 +139,9 @@ def non_max_suppression(
labels=(),
max_det=300,
nc=0, # number of classes (optional)
max_time_img=0.05,
max_nms=30000,
max_wh=7680,
):
"""
Perform non-maximum suppression (NMS) on a set of boxes, with support for masks and multiple labels per box.
@ -160,6 +163,9 @@ def non_max_suppression(
output by a dataloader, with each label being a tuple of (class_index, x1, y1, x2, y2).
max_det (int): The maximum number of boxes to keep after NMS.
nc (int): (optional) The number of classes output by the model. Any indices after this will be considered masks.
max_time_img (float): The maximum time (seconds) for processing one image.
max_nms (int): The maximum number of boxes into torchvision.ops.nms().
max_wh (int): The maximum box width and height in pixels
Returns:
(List[torch.Tensor]): A list of length batch_size, where each element is a tensor of
@ -185,9 +191,7 @@ def non_max_suppression(
# Settings
# min_wh = 2 # (pixels) minimum box width and height
max_wh = 7680 # (pixels) maximum box width and height
max_nms = 30000 # maximum number of boxes into torchvision.ops.nms()
time_limit = 0.5 + 0.05 * bs # seconds to quit after
time_limit = 0.5 + max_time_img * bs # seconds to quit after
redundant = True # require redundant detections
multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img)
merge = False # use merge-NMS