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Add docs Ultralytics 文档: - zh/index.md (#5871)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-10-21 20:31:45 +02:00 • committed by GitHub
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3
ultralytics/utils/metrics.py
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@ -48,8 +48,7 @@ def bbox_ioa(box1, box2, iou=False, eps=1e-7):
def box_iou(box1, box2, eps=1e-7):
"""
Calculate intersection-over-union (IoU) of boxes.
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Calculate intersection-over-union (IoU) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
Args:

8
ultralytics/utils/tal.py
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@ -61,8 +61,8 @@ class TaskAlignedAssigner(nn.Module):
"""
A task-aligned assigner for object detection.
This class assigns ground-truth (gt) objects to anchors based on the task-aligned metric,
which combines both classification and localization information.
This class assigns ground-truth (gt) objects to anchors based on the task-aligned metric, which combines both
classification and localization information.
Attributes:
topk (int): The number of top candidates to consider.
@ -85,8 +85,8 @@ class TaskAlignedAssigner(nn.Module):
@torch.no_grad()
def forward(self, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt):
"""
Compute the task-aligned assignment.
Reference https://github.com/Nioolek/PPYOLOE_pytorch/blob/master/ppyoloe/assigner/tal_assigner.py
Compute the task-aligned assignment. Reference code is available at
https://github.com/Nioolek/PPYOLOE_pytorch/blob/master/ppyoloe/assigner/tal_assigner.py.
Args:
pd_scores (Tensor): shape(bs, num_total_anchors, num_classes)