ultralytics 8.0.94 HUBDatasetStats() Segment and Pose support (#2450)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: JF Chen <k-2feng@hotmail.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Laughing-q <1185102784@qq.com>

ultralytics/yolo/v8/detect/train.py

@@ -32,7 +32,7 @@ class DetectionTrainer(BaseTrainer):
         gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
         return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == 'val', stride=gs)
 
-    def get_dataloader(self, dataset_path, batch_size, rank=0, mode='train'):
+    def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'):
         """TODO: manage splits differently."""
         # Calculate stride - check if model is initialized
         if self.args.v5loader:
@@ -62,8 +62,7 @@ class DetectionTrainer(BaseTrainer):
             LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with DataLoader shuffle, setting shuffle=False")
             shuffle = False
         workers = self.args.workers if mode == 'train' else self.args.workers * 2
-        dataloader = build_dataloader(dataset, batch_size, workers, shuffle, rank)
-        return dataloader
+        return build_dataloader(dataset, batch_size, workers, shuffle, rank)  # return dataloader
 
     def preprocess_batch(self, batch):
         """Preprocesses a batch of images by scaling and converting to float."""

ultralytics/yolo/v8/detect/val.py

@@ -144,7 +144,8 @@ class DetectionValidator(BaseValidator):
                 LOGGER.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i)))
 
         if self.args.plots:
-            self.confusion_matrix.plot(save_dir=self.save_dir, names=list(self.names.values()))
+            for normalize in True, False:
+                self.confusion_matrix.plot(save_dir=self.save_dir, names=self.names.values(), normalize=normalize)
 
     def _process_batch(self, detections, labels):
         """