diff --git a/test/test_utils/test_utils_nn.py b/test/test_utils/test_utils_nn.py
index ec533707..7207ca26 100644
--- a/test/test_utils/test_utils_nn.py
+++ b/test/test_utils/test_utils_nn.py
@@ -2,6 +2,7 @@
 """
 
 import itertools
+import shutil
 from pathlib import Path
 
 import numpy as np
@@ -523,11 +524,31 @@ def test_mixin_classes():
     assert save_path.is_file()
 
     loaded_model, _ = Model1D.from_checkpoint(save_path)
-
     assert repr(model_1d) == repr(loaded_model)
 
     save_path.unlink()
 
+    # test remote un-compressed model
+    save_path = Path(__file__).resolve().parents[1] / "tmp" / "test_remote_model"
+    save_path.mkdir(exist_ok=True, parents=True)
+    loaded_model, _ = Model1D.from_remote(
+        url="https://www.dropbox.com/scl/fi/5q5q0z0ta48ml0u2xtwm7/test-remote-model.pth?rlkey=2l2erhdnrfc4om0fqarokikb0&dl=1",
+        model_dir=save_path,
+    )
+    assert isinstance(loaded_model, Model1D)
+    shutil.rmtree(save_path)
+
+    # test remote compressed model
+    save_path = Path(__file__).resolve().parents[1] / "tmp" / "test_remote_model"
+    save_path.mkdir(exist_ok=True, parents=True)
+    loaded_model, _ = Model1D.from_remote(
+        url="https://www.dropbox.com/scl/fi/2eqhnagz1m0w0ka86uegr/test-remote-model.zip?rlkey=1mkuwhx4kykqmc7h4rnou46z0&dl=1",
+        model_dir=save_path,
+        compressed=True,
+    )
+    assert isinstance(loaded_model, Model1D)
+    shutil.rmtree(save_path)
+
     model_dummy = ModelDummy(12, CFG(out_channels=128))
     assert model_dummy.module_size == model_dummy.sizeof == 0
     assert model_dummy.module_size_ == model_dummy.sizeof_ == "0.0B"
diff --git a/torch_ecg/utils/utils_nn.py b/torch_ecg/utils/utils_nn.py
index 00ba397d..73cbb978 100644
--- a/torch_ecg/utils/utils_nn.py
+++ b/torch_ecg/utils/utils_nn.py
@@ -18,6 +18,7 @@
 from torch import Tensor, nn
 
 from ..cfg import CFG, DEFAULTS
+from .download import http_get
 from .misc import add_docstring, make_serializable
 from .utils_data import cls_to_bin
 
@@ -1043,7 +1044,9 @@ def from_checkpoint(
         Parameters
         ----------
         path : `path-like`
-            Path of the checkpoint.
+            Path to the checkpoint.
+            If it is a directory, then this directory should contain only one checkpoint file
+            (with the extension `.pth` or `.pt`).
         device : torch.device, optional
             Map location of the model parameters,
             defaults to "cuda" if available, otherwise "cpu".
@@ -1056,6 +1059,10 @@ def from_checkpoint(
             Auxiliary configs that are needed for data preprocessing, etc.
 
         """
+        if Path(path).is_dir():
+            candidates = list(Path(path).glob("*.pth")) + list(Path(path).glob("*.pt"))
+            assert len(candidates) == 1, "The directory should contain only one checkpoint file"
+            path = candidates[0]
         _device = device or DEFAULTS.device
         ckpt = torch.load(path, map_location=_device)
         aux_config = ckpt.get("train_config", None) or ckpt.get("config", None)
@@ -1071,6 +1078,39 @@ def from_checkpoint(
         model.load_state_dict(ckpt["model_state_dict"])
         return model, aux_config
 
+    @classmethod
+    def from_remote(
+        cls,
+        url: str,
+        model_dir: Union[str, bytes, os.PathLike],
+        filename: Optional[str] = None,
+        device: Optional[torch.device] = None,
+    ) -> Tuple[nn.Module, dict]:
+        """Load the model from the remote model.
+
+        Parameters
+        ----------
+        url : str
+            URL of the remote model.
+        model_dir : `path-like`
+            Path for downloading the model.
+        filename : str, optional
+            Filename of the model to save, defaults to the basename of the URL.
+        device : torch.device, optional
+            Map location of the model parameters,
+            defaults to "cuda" if available, otherwise "cpu".
+
+        Returns
+        -------
+        model : torch.nn.Module
+            The model loaded from a checkpoint.
+        aux_config : dict
+            Auxiliary configs that are needed for data preprocessing, etc.
+
+        """
+        http_get(url, model_dir, extract=True, filename=filename)
+        return cls.from_checkpoint(model_dir, device=device)
+
     def save(self, path: Union[str, bytes, os.PathLike], train_config: CFG) -> None:
         """Save the model to disk.