diff --git a/README.md b/README.md
index b340113..9d8e853 100644
--- a/README.md
+++ b/README.md
@@ -41,7 +41,6 @@ This repo holds the object detector and feature extractor for running things.
 > [!IMPORTANT]
 > If you have questions or find bugs or anything, you can contact us in our [organisation's discussion](https://github.com/orgs/emma-heriot-watt/discussions).
 
-
 ## Writing code and running things
 
 ### Run the server for the [Alexa Arena](https://github.com/amazon-science/alexa-arena)
diff --git a/pyproject.toml b/pyproject.toml
index 98df7ae..dd8bbb6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,11 +39,6 @@ cmd = "pytest -v --junitxml=pytest.xml --cov=src -m 'not slow and not multiproce
 help = "Update torch to use the latest CUDA version"
 shell = "python scripts/update_torch_cuda.py"
 
-[[tool.poe.tasks.postinstall]]
-help = 'Update torch and install maskrcnn-benchmark and scene-graph-benchmark'
-shell = """
-		pip install git+https://github.com/emma-simbot/scene_graph_benchmark
-		"""
 
 [tool.poetry.dependencies]
 python = ">=3.9,<3.10"
diff --git a/src/emma_perception/models/simbot_entity_classifier.py b/src/emma_perception/models/simbot_entity_classifier.py
index 741e5be..b3ab5b2 100644
--- a/src/emma_perception/models/simbot_entity_classifier.py
+++ b/src/emma_perception/models/simbot_entity_classifier.py
@@ -177,12 +177,12 @@ def __init__(
 
     def make_layers(self) -> Sequential:
         """Make a simple 2 layer MLP."""
-        layers = []
+        layers: list[torch.nn.Module] = []
 
-        layers.append(Linear(self._in_features, self._hidden_dim))  # type: ignore[arg-type]
-        layers.append(BatchNorm1d(self._hidden_dim))  # type: ignore[arg-type]
-        layers.append(Dropout(self._dropout))  # type: ignore[arg-type]
-        layers.append(Linear(self._hidden_dim, self._num_classes))  # type: ignore[arg-type]
+        layers.append(Linear(self._in_features, self._hidden_dim))
+        layers.append(BatchNorm1d(self._hidden_dim))
+        layers.append(Dropout(self._dropout))
+        layers.append(Linear(self._hidden_dim, self._num_classes))
 
         return Sequential(*layers)