diff --git a/models/common.py b/models/common.py
index 273e73d9e729..2fe99be8972b 100644
--- a/models/common.py
+++ b/models/common.py
@@ -40,13 +40,13 @@ def autopad(k, p=None, d=1):  # kernel, padding, dilation
 
 class Conv(nn.Module):
     # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
-    act = nn.SiLU()  # default activation
+    default_act = nn.SiLU()  # default activation
 
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
         super().__init__()
         self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
         self.bn = nn.BatchNorm2d(c2)
-        self.act = self.act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
+        self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
 
     def forward(self, x):
         return self.act(self.bn(self.conv(x)))
diff --git a/models/yolo.py b/models/yolo.py
index 1d0da2a6e010..ed21c067ee93 100644
--- a/models/yolo.py
+++ b/models/yolo.py
@@ -301,7 +301,7 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
     LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10}  {'module':<40}{'arguments':<30}")
     anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
     if act:
-        Conv.act = eval(act)  # redefine default activation, i.e. Conv.act = nn.SiLU()
+        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
         LOGGER.info(f"{colorstr('activation:')} {act}")  # print
     na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors  # number of anchors
     no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
diff --git a/utils/dataloaders.py b/utils/dataloaders.py
index 7aee0b891161..6cd1da6b9cf9 100644
--- a/utils/dataloaders.py
+++ b/utils/dataloaders.py
@@ -40,6 +40,7 @@
 VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes
 BAR_FORMAT = '{l_bar}{bar:10}{r_bar}{bar:-10b}'  # tqdm bar format
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv('RANK', -1))
 PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true'  # global pin_memory for dataloaders
 
 # Get orientation exif tag
@@ -139,7 +140,7 @@ def create_dataloader(path,
     sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
     loader = DataLoader if image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates
     generator = torch.Generator()
-    generator.manual_seed(0)
+    generator.manual_seed(6148914691236517205 + RANK)
     return loader(dataset,
                   batch_size=batch_size,
                   shuffle=shuffle and sampler is None,
@@ -1169,7 +1170,7 @@ def create_classification_dataloader(path,
     nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])
     sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
     generator = torch.Generator()
-    generator.manual_seed(0)
+    generator.manual_seed(6148914691236517205 + RANK)
     return InfiniteDataLoader(dataset,
                               batch_size=batch_size,
                               shuffle=shuffle and sampler is None,
diff --git a/utils/segment/dataloaders.py b/utils/segment/dataloaders.py
index 97ef8556068e..a63d6ec013fd 100644
--- a/utils/segment/dataloaders.py
+++ b/utils/segment/dataloaders.py
@@ -17,6 +17,8 @@
 from ..torch_utils import torch_distributed_zero_first
 from .augmentations import mixup, random_perspective
 
+RANK = int(os.getenv('RANK', -1))
+
 
 def create_dataloader(path,
                       imgsz,
@@ -61,8 +63,8 @@ def create_dataloader(path,
     nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers
     sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
     loader = DataLoader if image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates
-    # generator = torch.Generator()
-    # generator.manual_seed(0)
+    generator = torch.Generator()
+    generator.manual_seed(6148914691236517205 + RANK)
     return loader(
         dataset,
         batch_size=batch_size,
@@ -72,7 +74,7 @@ def create_dataloader(path,
         pin_memory=True,
         collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn,
         worker_init_fn=seed_worker,
-        # generator=generator,
+        generator=generator,
     ), dataset