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Update IoU capitalization (#8604)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Dean Mark <2552482+deanmark@users.noreply.github.com>
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Glenn Jocher 2024-03-02 22:12:23 +01:00 committed by GitHub
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8 changed files with 16 additions and 16 deletions
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2
docs/en/guides/yolo-performance-metrics.md
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@ -18,7 +18,7 @@ Performance metrics are key tools to evaluate the accuracy and efficiency of obj
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Ultralytics YOLOv8 Performance Metrics | MAP, F1 Score, Precision, IOU & Accuracy
<strong>Watch:</strong> Ultralytics YOLOv8 Performance Metrics | MAP, F1 Score, Precision, IoU & Accuracy
</p>
## Object Detection Metrics

4
docs/en/reference/utils/metrics.md
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@ -1,6 +1,6 @@
---
description: Explore Ultralytics YOLO metrics tools - from confusion matrix, detection metrics, pose metrics to box IOU. Learn how to compute and plot precision-recall curves.
keywords: Ultralytics, YOLO, YOLOv3, YOLOv4, metrics, confusion matrix, detection metrics, pose metrics, box IOU, mask IOU, plot precision-recall curves, compute average precision
description: Explore Ultralytics YOLO metrics tools - from confusion matrix, detection metrics, pose metrics to box IoU. Learn how to compute and plot precision-recall curves.
keywords: Ultralytics, YOLO, YOLOv3, YOLOv4, metrics, confusion matrix, detection metrics, pose metrics, box IoU, mask IoU, plot precision-recall curves, compute average precision
---
# Reference for `ultralytics/utils/metrics.py`

2
ultralytics/engine/exporter.py
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@ -1088,7 +1088,7 @@ class Exporter:
# Save the model
model = ct.models.MLModel(pipeline.spec, weights_dir=weights_dir)
model.input_description["image"] = "Input image"
model.input_description["iouThreshold"] = f"(optional) IOU threshold override (default: {nms.iouThreshold})"
model.input_description["iouThreshold"] = f"(optional) IoU threshold override (default: {nms.iouThreshold})"
model.input_description["confidenceThreshold"] = (
f"(optional) Confidence threshold override (default: {nms.confidenceThreshold})"
)

2
ultralytics/models/yolo/detect/val.py
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@ -36,7 +36,7 @@ class DetectionValidator(BaseValidator):
self.class_map = None
self.args.task = "detect"
self.metrics = DetMetrics(save_dir=self.save_dir, on_plot=self.on_plot)
self.iouv = torch.linspace(0.5, 0.95, 10) # iou vector for mAP@0.5:0.95
self.iouv = torch.linspace(0.5, 0.95, 10) # IoU vector for mAP@0.5:0.95
self.niou = self.iouv.numel()
self.lb = [] # for autolabelling

6
ultralytics/trackers/byte_tracker.py
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@ -235,7 +235,7 @@ class BYTETracker:
reset_id(): Resets the ID counter of STrack.
joint_stracks(tlista, tlistb): Combines two lists of stracks.
sub_stracks(tlista, tlistb): Filters out the stracks present in the second list from the first list.
remove_duplicate_stracks(stracksa, stracksb): Removes duplicate stracks based on IOU.
remove_duplicate_stracks(stracksa, stracksb): Removes duplicate stracks based on IoU.
"""
def __init__(self, args, frame_rate=30):
@ -373,7 +373,7 @@ class BYTETracker:
return [STrack(xyxy, s, c) for (xyxy, s, c) in zip(dets, scores, cls)] if len(dets) else [] # detections
def get_dists(self, tracks, detections):
"""Calculates the distance between tracks and detections using IOU and fuses scores."""
"""Calculates the distance between tracks and detections using IoU and fuses scores."""
dists = matching.iou_distance(tracks, detections)
# TODO: mot20
# if not self.args.mot20:
@ -428,7 +428,7 @@ class BYTETracker:
@staticmethod
def remove_duplicate_stracks(stracksa, stracksb):
"""Remove duplicate stracks with non-maximum IOU distance."""
"""Remove duplicate stracks with non-maximum IoU distance."""
pdist = matching.iou_distance(stracksa, stracksb)
pairs = np.where(pdist < 0.15)
dupa, dupb = [], []

6
ultralytics/utils/metrics.py
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@ -24,7 +24,7 @@ def bbox_ioa(box1, box2, iou=False, eps=1e-7):
Args:
box1 (np.ndarray): A numpy array of shape (n, 4) representing n bounding boxes.
box2 (np.ndarray): A numpy array of shape (m, 4) representing m bounding boxes.
iou (bool): Calculate the standard iou if True else return inter_area/box2_area.
iou (bool): Calculate the standard IoU if True else return inter_area/box2_area.
eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7.
Returns:
@ -194,7 +194,7 @@ def _get_covariance_matrix(boxes):
def probiou(obb1, obb2, CIoU=False, eps=1e-7):
"""
Calculate the prob iou between oriented bounding boxes, https://arxiv.org/pdf/2106.06072v1.pdf.
Calculate the prob IoU between oriented bounding boxes, https://arxiv.org/pdf/2106.06072v1.pdf.
Args:
obb1 (torch.Tensor): A tensor of shape (N, 5) representing ground truth obbs, with xywhr format.
@ -233,7 +233,7 @@ def probiou(obb1, obb2, CIoU=False, eps=1e-7):
def batch_probiou(obb1, obb2, eps=1e-7):
"""
Calculate the prob iou between oriented bounding boxes, https://arxiv.org/pdf/2106.06072v1.pdf.
Calculate the prob IoU between oriented bounding boxes, https://arxiv.org/pdf/2106.06072v1.pdf.
Args:
obb1 (torch.Tensor | np.ndarray): A tensor of shape (N, 5) representing ground truth obbs, with xywhr format.

4
ultralytics/utils/ops.py
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@ -147,7 +147,7 @@ def nms_rotated(boxes, scores, threshold=0.45):
Args:
boxes (torch.Tensor): (N, 5), xywhr.
scores (torch.Tensor): (N, ).
threshold (float): Iou threshold.
threshold (float): IoU threshold.
Returns:
"""
@ -287,7 +287,7 @@ def non_max_suppression(
# if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
# # Update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
# from .metrics import box_iou
# iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
# iou = box_iou(boxes[i], boxes) > iou_thres # IoU matrix
# weights = iou * scores[None] # box weights
# x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
# redundant = True # require redundant detections

6
ultralytics/utils/tal.py
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@ -121,7 +121,7 @@ class TaskAlignedAssigner(nn.Module):
return align_metric, overlaps
def iou_calculation(self, gt_bboxes, pd_bboxes):
"""Iou calculation for horizontal bounding boxes."""
"""IoU calculation for horizontal bounding boxes."""
return bbox_iou(gt_bboxes, pd_bboxes, xywh=False, CIoU=True).squeeze(-1).clamp_(0)
def select_topk_candidates(self, metrics, largest=True, topk_mask=None):
@ -231,7 +231,7 @@ class TaskAlignedAssigner(nn.Module):
@staticmethod
def select_highest_overlaps(mask_pos, overlaps, n_max_boxes):
"""
If an anchor box is assigned to multiple gts, the one with the highest IoI will be selected.
If an anchor box is assigned to multiple gts, the one with the highest IoU will be selected.
Args:
mask_pos (Tensor): shape(b, n_max_boxes, h*w)
@ -260,7 +260,7 @@ class TaskAlignedAssigner(nn.Module):
class RotatedTaskAlignedAssigner(TaskAlignedAssigner):
def iou_calculation(self, gt_bboxes, pd_bboxes):
"""Iou calculation for rotated bounding boxes."""
"""IoU calculation for rotated bounding boxes."""
return probiou(gt_bboxes, pd_bboxes).squeeze(-1).clamp_(0)
@staticmethod