diff --git a/ultralytics/yolo/utils/callbacks/base.py b/ultralytics/yolo/utils/callbacks/base.py
index 671c91f..d91d255 100644
--- a/ultralytics/yolo/utils/callbacks/base.py
+++ b/ultralytics/yolo/utils/callbacks/base.py
@@ -99,7 +99,8 @@ default_callbacks = {
 def add_integration_callbacks(trainer):
     from .clearml import callbacks as clearml_callbacks
     from .tb import callbacks as tb_callbacks
+    from .wb import callbacks as wb_callbacks
 
-    for x in tb_callbacks, clearml_callbacks:
+    for x in clearml_callbacks, tb_callbacks, wb_callbacks:
         for k, v in x.items():
             trainer.add_callback(k, v)  # add_callback(name, func)
diff --git a/ultralytics/yolo/utils/callbacks/clearml.py b/ultralytics/yolo/utils/callbacks/clearml.py
index 3cfd4b1..8c01cbd 100644
--- a/ultralytics/yolo/utils/callbacks/clearml.py
+++ b/ultralytics/yolo/utils/callbacks/clearml.py
@@ -16,7 +16,7 @@ def _log_images(imgs_dict, group="", step=0):
             task.get_logger().report_image(group, k, step, v)
 
 
-def on_train_start(trainer):
+def on_pretrain_routine_start(trainer):
     # TODO: reuse existing task
     task = Task.init(project_name=trainer.args.project if trainer.args.project != 'runs/train' else 'YOLOv8',
                      task_name=trainer.args.name,
@@ -48,7 +48,7 @@ def on_train_end(trainer):
 
 
 callbacks = {
-    "on_train_start": on_train_start,
+    "on_pretrain_routine_start": on_pretrain_routine_start,
     "on_train_epoch_end": on_train_epoch_end,
     "on_val_end": on_val_end,
     "on_train_end": on_train_end} if clearml else {}
diff --git a/ultralytics/yolo/utils/callbacks/tb.py b/ultralytics/yolo/utils/callbacks/tb.py
index b442424..294112a 100644
--- a/ultralytics/yolo/utils/callbacks/tb.py
+++ b/ultralytics/yolo/utils/callbacks/tb.py
@@ -8,7 +8,7 @@ def _log_scalars(scalars, step=0):
         writer.add_scalar(k, v, step)
 
 
-def on_train_start(trainer):
+def on_pretrain_routine_start(trainer):
     global writer
     writer = SummaryWriter(str(trainer.save_dir))
 
@@ -21,4 +21,7 @@ def on_val_end(trainer):
     _log_scalars(trainer.metrics, trainer.epoch + 1)
 
 
-callbacks = {"on_train_start": on_train_start, "on_val_end": on_val_end, "on_batch_end": on_batch_end}
+callbacks = {
+    "on_pretrain_routine_start": on_pretrain_routine_start,
+    "on_val_end": on_val_end,
+    "on_batch_end": on_batch_end}
diff --git a/ultralytics/yolo/utils/callbacks/wb.py b/ultralytics/yolo/utils/callbacks/wb.py
new file mode 100644
index 0000000..d0287d1
--- /dev/null
+++ b/ultralytics/yolo/utils/callbacks/wb.py
@@ -0,0 +1,46 @@
+from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params
+
+try:
+    import wandb
+
+    assert hasattr(wandb, '__version__')
+except (ImportError, AssertionError):
+    wandb = None
+
+
+def on_pretrain_routine_start(trainer):
+    wandb.init(project=trainer.args.project if trainer.args.project != 'runs/train' else 'YOLOv8',
+               name=trainer.args.name,
+               config=dict(trainer.args)) if not wandb.run else wandb.run
+
+
+def on_val_end(trainer):
+    wandb.run.log(trainer.metrics, step=trainer.epoch + 1)
+    if trainer.epoch == 0:
+        model_info = {
+            "model/parameters": get_num_params(trainer.model),
+            "model/GFLOPs": round(get_flops(trainer.model), 1),
+            "model/speed(ms)": round(trainer.validator.speed[1], 1)}
+        wandb.run.log(model_info, step=trainer.epoch + 1)
+
+
+def on_train_epoch_end(trainer):
+    wandb.run.log(trainer.label_loss_items(trainer.tloss, prefix="train"), step=trainer.epoch + 1)
+    if trainer.epoch == 1:
+        wandb.run.log({f.stem: wandb.Image(str(f))
+                       for f in trainer.save_dir.glob('train_batch*.jpg')},
+                      step=trainer.epoch + 1)
+
+
+def on_train_end(trainer):
+    art = wandb.Artifact(type="model", name=f"run_{wandb.run.id}_model")
+    if trainer.best.exists():
+        art.add_file(trainer.best)
+        wandb.run.log_artifact(art)
+
+
+callbacks = {
+    "on_pretrain_routine_start": on_pretrain_routine_start,
+    "on_train_epoch_end": on_train_epoch_end,
+    "on_val_end": on_val_end,
+    "on_train_end": on_train_end} if wandb else {}
diff --git a/ultralytics/yolo/utils/torch_utils.py b/ultralytics/yolo/utils/torch_utils.py
index a61c669..573a6e9 100644
--- a/ultralytics/yolo/utils/torch_utils.py
+++ b/ultralytics/yolo/utils/torch_utils.py
@@ -55,7 +55,7 @@ def DDP_model(model):
         return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
 
 
-def select_device(device='', batch_size=0, newline=True):
+def select_device(device='', batch_size=0, newline=False):
     # device = None or 'cpu' or 0 or '0' or '0,1,2,3'
     ver = git_describe() or ultralytics.__version__  # git commit or pip package version
     s = f'Ultralytics YOLO 🚀 {ver} Python-{platform.python_version()} torch-{torch.__version__} '
@@ -86,9 +86,7 @@ def select_device(device='', batch_size=0, newline=True):
         s += 'CPU\n'
         arg = 'cpu'
 
-    if not newline:
-        s = s.rstrip()
-    LOGGER.info(s)
+    LOGGER.info(s if newline else s.rstrip())
     return torch.device(arg)
 
 
@@ -150,6 +148,7 @@ def get_num_gradients(model):
 
 def get_flops(model, imgsz=640):
     try:
+        model = de_parallel(model)
         p = next(model.parameters())
         stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32  # max stride
         im = torch.empty((1, p.shape[1], stride, stride), device=p.device)  # input image in BCHW format