diff --git a/src/careamics/dataset_ng/patching_strategies/patch_specs_generator.py b/src/careamics/dataset_ng/patching_strategies/patch_specs_generator.py
index 4603179f..bd9b727c 100644
--- a/src/careamics/dataset_ng/patching_strategies/patch_specs_generator.py
+++ b/src/careamics/dataset_ng/patching_strategies/patch_specs_generator.py
@@ -1,20 +1,25 @@
 from collections.abc import Sequence
-from typing import Protocol
+from typing import ParamSpec, Protocol
 
 import numpy as np
+from numpy.typing import NDArray
 
 from ..patch_extractor import PatchSpecs
 
+P = ParamSpec("P")
 
-class PatchSpecsGenerator(Protocol):
+
+class PatchSpecsGenerator(Protocol[P]):
 
     def generate(
-        self, patch_size: Sequence[int], *args, **kwargs
+        self, patch_size: Sequence[int], *args: P.args, **kwargs: P.kwargs
     ) -> list[PatchSpecs]: ...
 
     # Should return the number of patches that will be produced for a set of args
     # Will be for mapped dataset length
-    def n_patches(self, patch_size: Sequence[int], *args, **kwargs): ...
+    def n_patches(
+        self, patch_size: Sequence[int], *args: P.args, **kwargs: P.kwargs
+    ) -> int: ...
 
 
 class RandomPatchSpecsGenerator:
@@ -22,7 +27,7 @@ class RandomPatchSpecsGenerator:
     def __init__(self, data_shapes: Sequence[Sequence[int]]):
         self.data_shapes = data_shapes
 
-    def generate(self, patch_size: Sequence[int], seed: int):
+    def generate(self, patch_size: Sequence[int], seed: int) -> list[PatchSpecs]:
         rng = np.random.default_rng(seed=seed)
         patch_specs: list[PatchSpecs] = []
         for data_idx, data_shape in enumerate(self.data_shapes):
@@ -50,8 +55,10 @@ def generate(self, patch_size: Sequence[int], seed: int):
             patch_specs.extend(data_patch_specs)
         return patch_specs
 
-    def n_patches(self, patch_size: Sequence[int], seed: int):
-        n_sample_patches = np.array(
+    # NOTE: enerate and n_patches methods must have matching signatures
+    #   as dictated by protocol
+    def n_patches(self, patch_size: Sequence[int], seed: int) -> int:
+        n_sample_patches: NDArray[np.int_] = np.array(
             [
                 self._n_patches_in_sample(patch_size, data_shape[-len(patch_size) :])
                 for data_shape in self.data_shapes
@@ -62,10 +69,12 @@ def n_patches(self, patch_size: Sequence[int], seed: int):
             [data_shape[0] for data_shape in self.data_shapes], dtype=int
         )
         n_data_patches = n_samples * n_sample_patches
-        return n_data_patches.sum()
+        return int(n_data_patches.sum())
 
     @staticmethod
-    def _n_patches_in_sample(patch_size: Sequence[int], spatial_shape: Sequence[int]):
+    def _n_patches_in_sample(
+        patch_size: Sequence[int], spatial_shape: Sequence[int]
+    ) -> int:
         if len(patch_size) != len(spatial_shape):
             raise ValueError(
                 "Number of patch dimension do not match the number of spatial "