Merge branch 'main' into quan

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.3.8"
+__version__ = "8.3.9"
 
 import os
 

ultralytics/engine/trainer.py

@@ -469,11 +469,9 @@ class BaseTrainer:
 
         if RANK in {-1, 0}:
             # Do final val with best.pt
-            epochs = epoch - self.start_epoch + 1  # total training epochs
-            seconds = time.time() - self.train_time_start  # total training seconds
-            LOGGER.info(f"\n{epochs} epochs completed in {seconds / 3600:.3f} hours.")
+            seconds = time.time() - self.train_time_start
+            LOGGER.info(f"\n{epoch - self.start_epoch + 1} epochs completed in {seconds / 3600:.3f} hours.")
             self.final_eval()
-            self.validator.metrics.training = {"epochs": epochs, "seconds": seconds}  # add training speed
             if self.args.plots:
                 self.plot_metrics()
             self.run_callbacks("on_train_end")
@@ -504,7 +502,7 @@ class BaseTrainer:
         """Read results.csv into a dict using pandas."""
         import pandas as pd  # scope for faster 'import ultralytics'
 
-        return {k.strip(): v for k, v in pd.read_csv(self.csv).to_dict(orient="list").items()}
+        return pd.read_csv(self.csv).to_dict(orient="list")
 
     def save_model(self):
         """Save model training checkpoints with additional metadata."""
@@ -654,10 +652,11 @@ class BaseTrainer:
     def save_metrics(self, metrics):
         """Saves training metrics to a CSV file."""
         keys, vals = list(metrics.keys()), list(metrics.values())
-        n = len(metrics) + 1  # number of cols
-        s = "" if self.csv.exists() else (("%23s," * n % tuple(["epoch"] + keys)).rstrip(",") + "\n")  # header
+        n = len(metrics) + 2  # number of cols
+        s = "" if self.csv.exists() else (("%s," * n % tuple(["epoch", "time"] + keys)).rstrip(",") + "\n")  # header
+        t = time.time() - self.train_time_start
         with open(self.csv, "a") as f:
-            f.write(s + ("%23.5g," * n % tuple([self.epoch + 1] + vals)).rstrip(",") + "\n")
+            f.write(s + ("%.6g," * n % tuple([self.epoch + 1, t] + vals)).rstrip(",") + "\n")
 
     def plot_metrics(self):
         """Plot and display metrics visually."""