diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f390076..745b9a5 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -6,7 +6,7 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/pycqa/isort
-    rev: 5.10.1
+    rev: 5.12.0
     hooks:
       - id: isort
         args: [--settings-path, pyproject.toml]
diff --git a/pyproject.toml b/pyproject.toml
index 6122a80..dffca27 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,7 +14,7 @@ python = "^3.7"
 # Python lack of functionalities from future versions
 importlib-metadata = { version = "*", python = "<3.8" }
 
-neptune-client = ">=0.10.0"
+neptune-client = ">=0.16.16"
 numpy = "<1.24.0"
 fvcore = "<0.1.5.post20221220"
 
diff --git a/src/neptune_detectron2/impl/__init__.py b/src/neptune_detectron2/impl/__init__.py
index 3c4d43a..68da0b0 100644
--- a/src/neptune_detectron2/impl/__init__.py
+++ b/src/neptune_detectron2/impl/__init__.py
@@ -18,6 +18,8 @@
 
 The class is used for automatic metadata logging to Neptune, during training and validation of detectron2 models."""
 
+from __future__ import annotations
+
 __all__ = [
     "__version__",
     "NeptuneHook",
@@ -43,8 +45,10 @@
 import detectron2
 from detectron2.checkpoint import Checkpointer
 from detectron2.engine import hooks
+from neptune.new.handler import Handler
 from neptune.new.metadata_containers import Run
 from neptune.new.types import File
+from neptune.new.utils import stringify_unsupported
 from torch.nn import Module
 
 from neptune_detectron2.impl.version import __version__
@@ -56,7 +60,7 @@ class NeptuneHook(hooks.HookBase):
     """Hook implementation that sends the logs to Neptune.
 
     Args:
-        run: Pass a Neptune run object if you want to continue logging to an existing run.
+        run: Pass a Neptune run or namespace handler object if you want to continue logging to an existing run.
             Learn more about resuming runs in the docs: https://docs.neptune.ai/logging/to_existing_object
         base_namespace: In the Neptune run, the root namespace that will contain all the logged metadata.
         smoothing_window_size: How often NeptuneHook should log metrics (and checkpoints, if
@@ -90,7 +94,7 @@ class NeptuneHook(hooks.HookBase):
     def __init__(
         self,
         *,
-        run: Optional[Run] = None,
+        run: Optional[Run | Handler] = None,
         base_namespace: str = "training",
         smoothing_window_size: int = 20,
         log_model: bool = False,
@@ -103,15 +107,17 @@ def __init__(
 
         self._verify_window_size()
 
-        self._run = neptune.init_run(**kwargs) if not isinstance(run, Run) else run
+        self._run = neptune.init_run(**kwargs) if not run else run
 
-        verify_type("run", self._run, Run)
+        verify_type("run", self._run, (Run, Handler))
 
         if base_namespace.endswith("/"):
-            self._base_namespace = base_namespace[:-1]
+            base_namespace = base_namespace[:-1]
 
         self.base_handler = self._run[base_namespace]
 
+        self._root_object = self._run.get_root_object() if isinstance(self._run, Handler) else self._run
+
     def _verify_window_size(self) -> None:
         if self._window_size <= 0:
             raise ValueError(f"Update freq should be greater than 0. Got {self._window_size}.")
@@ -119,11 +125,11 @@ def _verify_window_size(self) -> None:
             raise TypeError(f"Smoothing window size should be of type int. Got {type(self._window_size)} instead.")
 
     def _log_integration_version(self) -> None:
-        self.base_handler[INTEGRATION_VERSION_KEY] = detectron2.__version__
+        self._root_object[INTEGRATION_VERSION_KEY] = detectron2.__version__
 
     def _log_config(self) -> None:
         if hasattr(self.trainer, "cfg") and isinstance(self.trainer.cfg, dict):
-            self.base_handler["config"] = self.trainer.cfg
+            self.base_handler["config"] = stringify_unsupported(self.trainer.cfg)
 
     def _log_model(self) -> None:
         if hasattr(self.trainer, "model") and isinstance(self.trainer.model, Module):
@@ -148,7 +154,7 @@ def _log_checkpoint(self, final: bool = False) -> None:
     def _log_metrics(self) -> None:
         storage = detectron2.utils.events.get_event_storage()
         for k, (v, _) in storage.latest_with_smoothing_hint(self._window_size).items():
-            self.base_handler[f"metrics/{k}"].log(v)
+            self.base_handler[f"metrics/{k}"].append(v)
 
     def _can_save_checkpoint(self) -> bool:
         return hasattr(self.trainer, "checkpointer") and isinstance(self.trainer.checkpointer, Checkpointer)
@@ -177,5 +183,5 @@ def after_train(self) -> None:
         if self.log_model:
             self._log_checkpoint(final=True)
 
-        self._run.sync()
-        self._run.stop()
+        self._root_object.sync()
+        self._root_object.stop()