diff --git a/docs/en/guides/yolo-performance-metrics.md b/docs/en/guides/yolo-performance-metrics.md
index d88b2ae7..a34f161e 100644
--- a/docs/en/guides/yolo-performance-metrics.md
+++ b/docs/en/guides/yolo-performance-metrics.md
@@ -18,7 +18,7 @@ Performance metrics are key tools to evaluate the accuracy and efficiency of obj
allowfullscreen>
- Watch: Ultralytics YOLOv8 Performance Metrics | MAP, F1 Score, Precision, IOU & Accuracy
+ Watch: Ultralytics YOLOv8 Performance Metrics | MAP, F1 Score, Precision, IoU & Accuracy
## Object Detection Metrics
diff --git a/docs/en/reference/utils/metrics.md b/docs/en/reference/utils/metrics.md
index 6851ae98..21c85066 100644
--- a/docs/en/reference/utils/metrics.md
+++ b/docs/en/reference/utils/metrics.md
@@ -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`
diff --git a/ultralytics/engine/exporter.py b/ultralytics/engine/exporter.py
index 1c6148f6..23b5caca 100644
--- a/ultralytics/engine/exporter.py
+++ b/ultralytics/engine/exporter.py
@@ -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})"
)
diff --git a/ultralytics/models/yolo/detect/val.py b/ultralytics/models/yolo/detect/val.py
index 4ca307b8..8226cd69 100644
--- a/ultralytics/models/yolo/detect/val.py
+++ b/ultralytics/models/yolo/detect/val.py
@@ -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
diff --git a/ultralytics/trackers/byte_tracker.py b/ultralytics/trackers/byte_tracker.py
index 7e10b8d8..01cbca97 100644
--- a/ultralytics/trackers/byte_tracker.py
+++ b/ultralytics/trackers/byte_tracker.py
@@ -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 = [], []
diff --git a/ultralytics/utils/metrics.py b/ultralytics/utils/metrics.py
index 85f53944..235db567 100644
--- a/ultralytics/utils/metrics.py
+++ b/ultralytics/utils/metrics.py
@@ -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.
diff --git a/ultralytics/utils/ops.py b/ultralytics/utils/ops.py
index fb346a96..439f9440 100644
--- a/ultralytics/utils/ops.py
+++ b/ultralytics/utils/ops.py
@@ -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
diff --git a/ultralytics/utils/tal.py b/ultralytics/utils/tal.py
index 80fee091..9cee0500 100644
--- a/ultralytics/utils/tal.py
+++ b/ultralytics/utils/tal.py
@@ -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