Removed manual accounting of field's count

segment/train.py

@@ -348,8 +348,13 @@ def lf(x):
             train_loader.sampler.set_epoch(epoch)
         pbar = enumerate(train_loader)
         LOGGER.info(
-            ("\n" + "%11s" * 8)
-            % ("Epoch", "GPU_mem", "box_loss", "seg_loss", "obj_loss", "cls_loss", "Instances", "Size")
+            "\n"
+            + "".join(
+                [
+                    f"{el:>11}"
+                    for el in ["Epoch", "GPU_mem", "box_loss", "seg_loss", "obj_loss", "cls_loss", "Instances", "Size"]
+                ]
+            )
         )
         if RANK in {-1, 0}:
             pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT)  # progress bar

segment/val.py

@@ -259,18 +259,25 @@ def run(
     if isinstance(names, (list, tuple)):  # old format
         names = dict(enumerate(names))
     class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
-    s = ("%22s" + "%11s" * 10) % (
-        "Class",
-        "Images",
-        "Instances",
-        "Box(P",
-        "R",
-        "mAP50",
-        "mAP50-95)",
-        "Mask(P",
-        "R",
-        "mAP50",
-        "mAP50-95)",
+    s = "".join(
+        [
+            f"{el:>11}" if idx > 0 else f"{el:>22}"
+            for idx, el in enumerate(
+                [
+                    "Class",
+                    "Images",
+                    "Instances",
+                    "Box(P",
+                    "R",
+                    "mAP50",
+                    "mAP50-95)",
+                    "Mask(P",
+                    "R",
+                    "mAP50",
+                    "mAP50-95)",
+                ]
+            )
+        ]
     )
     dt = Profile(device=device), Profile(device=device), Profile(device=device)
     metrics = Metrics()

train.py

@@ -352,7 +352,12 @@ def lf(x):
         if RANK != -1:
             train_loader.sampler.set_epoch(epoch)
         pbar = enumerate(train_loader)
-        LOGGER.info(("\n" + "%11s" * 7) % ("Epoch", "GPU_mem", "box_loss", "obj_loss", "cls_loss", "Instances", "Size"))
+        LOGGER.info(
+            "\n"
+            + "".join(
+                [f"{el:>11}" for el in ["Epoch", "GPU_mem", "box_loss", "obj_loss", "cls_loss", "Instances", "Size"]]
+            )
+        )
         if RANK in {-1, 0}:
             pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT)  # progress bar
         optimizer.zero_grad()

val.py

@@ -214,7 +214,12 @@ def run(
     if isinstance(names, (list, tuple)):  # old format
         names = dict(enumerate(names))
     class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
-    s = ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "P", "R", "mAP50", "mAP50-95")
+    s = "".join(
+        [
+            f"{el:>11}" if idx > 0 else f"{el:>22}"
+            for idx, el in enumerate(["Class", "Images", "Instances", "P", "R", "mAP50", "mAP50-95"])
+        ]
+    )
     tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
     dt = Profile(device=device), Profile(device=device), Profile(device=device)  # profiling times
     loss = torch.zeros(3, device=device)