Update IoU capitalization (#8604)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Dean Mark <2552482+deanmark@users.noreply.github.com>

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>
   </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

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`

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})"
         )

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
 

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 = [], []

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.

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

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