Docs updates: Add Explorer to tab, YOLOv5 in Guides and Usage in Quickstart (#7438)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Haixuan Xavier Tao <tao.xavier@outlook.com>

ultralytics/models/yolo/detect/val.py

@@ -87,6 +87,7 @@ class DetectionValidator(BaseValidator):
                                        max_det=self.args.max_det)
 
     def _prepare_batch(self, si, batch):
+        """Prepares a batch of images and annotations for validation."""
         idx = batch['batch_idx'] == si
         cls = batch['cls'][idx].squeeze(-1)
         bbox = batch['bboxes'][idx]
@@ -100,6 +101,7 @@ class DetectionValidator(BaseValidator):
         return prepared_batch
 
     def _prepare_pred(self, pred, pbatch):
+        """Prepares a batch of images and annotations for validation."""
         predn = pred.clone()
         ops.scale_boxes(pbatch['imgsz'], predn[:, :4], pbatch['ori_shape'],
                         ratio_pad=pbatch['ratio_pad'])  # native-space pred

ultralytics/models/yolo/obb/predict.py

@@ -23,6 +23,7 @@ class OBBPredictor(DetectionPredictor):
     """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """Initializes OBBPredictor with optional model and data configuration overrides."""
         super().__init__(cfg, overrides, _callbacks)
         self.args.task = 'obb'
 

ultralytics/models/yolo/obb/val.py

@@ -65,6 +65,7 @@ class OBBValidator(DetectionValidator):
         return self.match_predictions(detections[:, 5], gt_cls, iou)
 
     def _prepare_batch(self, si, batch):
+        """Prepares and returns a batch for OBB validation."""
         idx = batch['batch_idx'] == si
         cls = batch['cls'][idx].squeeze(-1)
         bbox = batch['bboxes'][idx]
@@ -78,6 +79,7 @@ class OBBValidator(DetectionValidator):
         return prepared_batch
 
     def _prepare_pred(self, pred, pbatch):
+        """Prepares and returns a batch for OBB validation with scaled and padded bounding boxes."""
         predn = pred.clone()
         ops.scale_boxes(pbatch['imgsz'], predn[:, :4], pbatch['ori_shape'], ratio_pad=pbatch['ratio_pad'],
                         xywh=True)  # native-space pred

ultralytics/models/yolo/pose/val.py

@@ -69,6 +69,7 @@ class PoseValidator(DetectionValidator):
         self.stats = dict(tp_p=[], tp=[], conf=[], pred_cls=[], target_cls=[])
 
     def _prepare_batch(self, si, batch):
+        """Prepares a batch for processing by converting keypoints to float and moving to device."""
         pbatch = super()._prepare_batch(si, batch)
         kpts = batch['keypoints'][batch['batch_idx'] == si]
         h, w = pbatch['imgsz']
@@ -80,6 +81,7 @@ class PoseValidator(DetectionValidator):
         return pbatch
 
     def _prepare_pred(self, pred, pbatch):
+        """Prepares and scales keypoints in a batch for pose processing."""
         predn = super()._prepare_pred(pred, pbatch)
         nk = pbatch['kpts'].shape[1]
         pred_kpts = predn[:, 6:].view(len(predn), nk, -1)

ultralytics/models/yolo/segment/val.py

@@ -72,12 +72,14 @@ class SegmentationValidator(DetectionValidator):
         return p, proto
 
     def _prepare_batch(self, si, batch):
+        """Prepares a batch for training or inference by processing images and targets."""
         prepared_batch = super()._prepare_batch(si, batch)
         midx = [si] if self.args.overlap_mask else batch['batch_idx'] == si
         prepared_batch['masks'] = batch['masks'][midx]
         return prepared_batch
 
     def _prepare_pred(self, pred, pbatch, proto):
+        """Prepares a batch for training or inference by processing images and targets."""
         predn = super()._prepare_pred(pred, pbatch)
         pred_masks = self.process(proto, pred[:, 6:], pred[:, :4], shape=pbatch['imgsz'])
         return predn, pred_masks