ultralytics 8.0.43 optimized Results class and fixes (#1069)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Alexander Duda <Alexander.Duda@me.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

ultralytics/yolo/v8/detect/predict.py

@@ -56,7 +56,7 @@ class DetectionPredictor(BasePredictor):
 
         det = results[idx].boxes  # TODO: make boxes inherit from tensors
         if len(det) == 0:
-            return log_string
+            return f'{log_string}(no detections), '
         for c in det.cls.unique():
             n = (det.cls == c).sum()  # detections per class
             log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "

ultralytics/yolo/v8/detect/train.py

@@ -1,6 +1,7 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 from copy import copy
 
+import numpy as np
 import torch
 import torch.nn as nn
 
@@ -12,7 +13,7 @@ from ultralytics.yolo.engine.trainer import BaseTrainer
 from ultralytics.yolo.utils import DEFAULT_CFG, RANK, colorstr
 from ultralytics.yolo.utils.loss import BboxLoss
 from ultralytics.yolo.utils.ops import xywh2xyxy
-from ultralytics.yolo.utils.plotting import plot_images, plot_results
+from ultralytics.yolo.utils.plotting import plot_images, plot_labels, plot_results
 from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
 from ultralytics.yolo.utils.torch_utils import de_parallel
 
@@ -102,6 +103,11 @@ class DetectionTrainer(BaseTrainer):
     def plot_metrics(self):
         plot_results(file=self.csv)  # save results.png
 
+    def plot_training_labels(self):
+        boxes = np.concatenate([lb['bboxes'] for lb in self.train_loader.dataset.labels], 0)
+        cls = np.concatenate([lb['cls'] for lb in self.train_loader.dataset.labels], 0)
+        plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir)
+
 
 # Criterion class for computing training losses
 class Loss:

ultralytics/yolo/v8/segment/predict.py

@@ -59,7 +59,7 @@ class SegmentationPredictor(DetectionPredictor):
 
         result = results[idx]
         if len(result) == 0:
-            return log_string
+            return f'{log_string}(no detections), '
         det, mask = result.boxes, result.masks  # getting tensors TODO: mask mask,box inherit for tensor
 
         # Print results