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Add Dockerfile-conda FROM continuumio/miniconda3:latest (#4706)

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Glenn Jocher 2023-09-03 23:44:11 +02:00 • committed by GitHub
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ultralytics/models/utils/ops.py
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@ -14,11 +14,11 @@ class HungarianMatcher(nn.Module):
A module implementing the HungarianMatcher, which is a differentiable module to solve the assignment problem in
an end-to-end fashion.
HungarianMatcher performs optimal assignment over predicted and ground truth bounding boxes using a cost function
that considers classification scores, bounding box coordinates, and optionally, mask predictions.
HungarianMatcher performs optimal assignment over the predicted and ground truth bounding boxes using a cost
function that considers classification scores, bounding box coordinates, and optionally, mask predictions.
Attributes:
cost_gain (dict): Dictionary of cost coefficients for different components: 'class', 'bbox', 'giou', 'mask', and 'dice'.
cost_gain (dict): Dictionary of cost coefficients: 'class', 'bbox', 'giou', 'mask', and 'dice'.
use_fl (bool): Indicates whether to use Focal Loss for the classification cost calculation.
with_mask (bool): Indicates whether the model makes mask predictions.
num_sample_points (int): The number of sample points used in mask cost calculation.
@ -26,8 +26,8 @@ class HungarianMatcher(nn.Module):
gamma (float): The gamma factor in Focal Loss calculation.
Methods:
forward(pred_bboxes, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=None, gt_mask=None): Computes the assignment
between predictions and ground truths for a batch.
forward(pred_bboxes, pred_scores, gt_bboxes, gt_cls, gt_groups, masks=None, gt_mask=None): Computes the
assignment between predictions and ground truths for a batch.
_cost_mask(bs, num_gts, masks=None, gt_mask=None): Computes the mask cost and dice cost if masks are predicted.
"""