diff --git a/pyproject.toml b/pyproject.toml
index 14e3d2f..b253de9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,8 +25,8 @@ dependencies = [
   "soundevent[audio,geometry,plot]>=2.10.0",
   "soundfile>=0.12.1",
   "tensorboard>=2.16.2",
-  "torch>=1.13.1",
-  "torchaudio>=1.13.1",
+  "torch>=2.0.0",
+  "torchaudio>=2.0.0",
   "xarray>=2024.0.0",
 ]
 requires-python = ">=3.10,<3.14"
diff --git a/src/batdetect2/models/blocks.py b/src/batdetect2/models/blocks.py
index 1cd3169..62bf6c7 100644
--- a/src/batdetect2/models/blocks.py
+++ b/src/batdetect2/models/blocks.py
@@ -70,11 +70,16 @@ __all__ = [
     "FreqCoordConvUpBlock",
     "StandardConvUpBlock",
     "SelfAttention",
+    "EfficientSelfAttention",
+    "VerticalMean",
     "ConvConfig",
+    "EfficientSelfAttentionConfig",
     "FreqCoordConvDownConfig",
     "StandardConvDownConfig",
     "FreqCoordConvUpConfig",
     "StandardConvUpConfig",
+    "VerticalConvConfig",
+    "VerticalMeanConfig",
     "LayerConfig",
     "build_layer",
 ]
@@ -580,6 +585,10 @@ class VerticalConv(Block):
         """
         return F.relu_(self.bn(self.conv(x)))
 
+    def get_output_height(self, input_height: int) -> int:
+        """Return the collapsed output height."""
+        return 1
+
     @block_registry.register(VerticalConvConfig)
     @staticmethod
     def from_config(
@@ -664,6 +673,10 @@ class VerticalMean(Block):
         x = self.conv(x)
         return F.relu(self.batch_norm(x), inplace=True)
 
+    def get_output_height(self, input_height: int) -> int:
+        """Return the collapsed output height."""
+        return 1
+
     @block_registry.register(VerticalMeanConfig)
     @staticmethod
     def from_config(
@@ -1155,13 +1168,16 @@ LayerConfig = Annotated[
     | FreqCoordConvUpConfig
     | StandardConvUpConfig
     | SelfAttentionConfig
+    | EfficientSelfAttentionConfig
+    | VerticalConvConfig
+    | VerticalMeanConfig
     | LayerGroupConfig,
     Field(discriminator="name"),
 ]
 """Type alias for the discriminated union of block configuration models."""
 
 
-class LayerGroup(nn.Module):
+class LayerGroup(Block):
     """Sequential chain of blocks that acts as a single composite block.
 
     Wraps multiple ``Block`` instances in an ``nn.Sequential`` container,
diff --git a/src/batdetect2/models/bottleneck.py b/src/batdetect2/models/bottleneck.py
index d871754..9ebffcd 100644
--- a/src/batdetect2/models/bottleneck.py
+++ b/src/batdetect2/models/bottleneck.py
@@ -198,7 +198,9 @@ class BottleneckConfig(BaseConfig):
 
     channels: int
     frequency_aggregation: FrequencyAggregationLayerConfig = Field(
-        default_factory=lambda: VerticalConvConfig(channels=256)
+        default_factory=lambda data: VerticalConvConfig(
+            channels=data["channels"]
+        )
     )
     layers: List[BottleneckLayerConfig] = Field(default_factory=list)
 
@@ -301,12 +303,22 @@ def build_bottleneck(
     """
     config = config or DEFAULT_BOTTLENECK_CONFIG
 
-    current_channels = in_channels
-    current_height = input_height
+    frequency_aggregator = build_frequency_aggregation(
+        input_height=input_height,
+        in_channels=in_channels,
+        config=config.frequency_aggregation,
+    )
+
+    current_channels = frequency_aggregator.out_channels
+    current_height = frequency_aggregator.get_output_height(input_height)
+    assert current_height == 1, (
+        "Bottleneck frequency aggregation should collapse spectrogram height"
+    )
 
     layers = []
 
     for layer_config in config.layers:
+        previous_height = current_height
         layer = build_layer(
             input_height=current_height,
             in_channels=current_channels,
@@ -314,21 +326,15 @@ def build_bottleneck(
         )
         current_height = layer.get_output_height(current_height)
         current_channels = layer.out_channels
-        assert current_height == input_height, (
+        assert current_height == previous_height, (
             "Bottleneck layers should not change the spectrogram height"
         )
         layers.append(layer)
 
-    frequency_aggregator = build_frequency_aggregation(
-        input_height=input_height,
-        in_channels=current_channels,
-        config=config.frequency_aggregation,
-    )
-
     return Bottleneck(
         input_height=input_height,
         in_channels=in_channels,
-        out_channels=config.channels,
+        out_channels=current_channels,
         frequency_aggregator=frequency_aggregator,
         layers=layers,
     )
diff --git a/tests/test_model.py b/tests/test_model.py
index 43b6eac..4bdfdbc 100644
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -10,6 +10,8 @@ from hypothesis import strategies as st
 
 from batdetect2 import api
 from batdetect2.detector import parameters
+from batdetect2.models.backbones import UNetBackboneConfig
+from batdetect2.train import load_model_from_checkpoint
 
 
 @settings(deadline=None, max_examples=5)
@@ -72,3 +74,12 @@ def test_can_import_model_without_pickle_on_test_data(
             model=model_with_pickle,
         )
         assert predictions_without_pickle == predictions_with_pickle
+
+
+def test_bundled_checkpoint_loads_with_current_config_schema() -> None:
+    """Bundled checkpoints remain loadable after config schema changes."""
+    model, configs = load_model_from_checkpoint()
+
+    assert model.class_names
+    assert isinstance(configs.model.architecture, UNetBackboneConfig)
+    assert configs.model.architecture.bottleneck.frequency_aggregation.name
diff --git a/tests/test_models/test_blocks.py b/tests/test_models/test_blocks.py
index 57315d5..e862ef9 100644
--- a/tests/test_models/test_blocks.py
+++ b/tests/test_models/test_blocks.py
@@ -4,6 +4,8 @@ import torch
 from batdetect2.models.blocks import (
     ConvBlock,
     ConvConfig,
+    EfficientSelfAttention,
+    EfficientSelfAttentionConfig,
     FreqCoordConvDownBlock,
     FreqCoordConvDownConfig,
     FreqCoordConvUpBlock,
@@ -18,6 +20,8 @@ from batdetect2.models.blocks import (
     StandardConvUpConfig,
     VerticalConv,
     VerticalConvConfig,
+    VerticalMean,
+    VerticalMeanConfig,
     build_layer,
 )
 
@@ -99,6 +103,20 @@ def test_vertical_conv_forward_shape(dummy_input):
     assert block.out_channels == out_channels
 
 
+def test_vertical_mean_forward_shape(dummy_input):
+    """Test that VerticalMean collapses the height dimension to 1."""
+    in_channels = dummy_input.size(1)
+    input_height = dummy_input.size(2)
+    out_channels = 32
+
+    block = VerticalMean(in_channels, out_channels, input_height)
+    output = block(dummy_input)
+
+    assert output.shape == (2, out_channels, 1, 32)
+    assert block.out_channels == out_channels
+    assert block.get_output_height(input_height) == 1
+
+
 def test_self_attention_forward_shape(dummy_bottleneck_input):
     """Test that SelfAttention maintains the exact shape."""
     in_channels = dummy_bottleneck_input.size(1)
@@ -113,6 +131,20 @@ def test_self_attention_forward_shape(dummy_bottleneck_input):
     assert block.out_channels == in_channels
 
 
+def test_efficient_self_attention_forward_shape(dummy_bottleneck_input):
+    """Test that EfficientSelfAttention maintains the exact shape."""
+    in_channels = dummy_bottleneck_input.size(1)
+    attention_channels = 32
+
+    block = EfficientSelfAttention(
+        in_channels=in_channels, attention_channels=attention_channels
+    )
+    output = block(dummy_bottleneck_input)
+
+    assert output.shape == dummy_bottleneck_input.shape
+    assert block.out_channels == in_channels
+
+
 def test_self_attention_weights(dummy_bottleneck_input):
     """Test that attention weights sum to 1 over the time sequence."""
     in_channels = dummy_bottleneck_input.size(1)
@@ -131,6 +163,51 @@ def test_self_attention_weights(dummy_bottleneck_input):
     assert torch.allclose(sum_weights, torch.ones_like(sum_weights), atol=1e-5)
 
 
+def test_efficient_self_attention_matches_self_attention_with_copied_weights(
+    dummy_bottleneck_input,
+):
+    """Temporarily compare efficient and original attention outputs."""
+    in_channels = dummy_bottleneck_input.size(1)
+    attention_channels = 32
+
+    block = SelfAttention(
+        in_channels=in_channels,
+        attention_channels=attention_channels,
+    )
+    efficient_block = EfficientSelfAttention(
+        in_channels=in_channels,
+        attention_channels=attention_channels,
+    )
+
+    # Match the fused QKV projection to the original separate projections.
+    with torch.no_grad():
+        efficient_block.qkv_proj.weight[:attention_channels].copy_(
+            block.query_fun.weight
+        )
+        efficient_block.qkv_proj.bias[:attention_channels].copy_(
+            block.query_fun.bias
+        )
+        efficient_block.qkv_proj.weight[
+            attention_channels : 2 * attention_channels
+        ].copy_(block.key_fun.weight)
+        efficient_block.qkv_proj.bias[
+            attention_channels : 2 * attention_channels
+        ].copy_(block.key_fun.bias)
+        efficient_block.qkv_proj.weight[2 * attention_channels :].copy_(
+            block.value_fun.weight
+        )
+        efficient_block.qkv_proj.bias[2 * attention_channels :].copy_(
+            block.value_fun.bias
+        )
+        efficient_block.pro_fun.weight.copy_(block.pro_fun.weight)
+        efficient_block.pro_fun.bias.copy_(block.pro_fun.bias)
+
+    output = block(dummy_bottleneck_input)
+    efficient_output = efficient_block(dummy_bottleneck_input)
+
+    torch.testing.assert_close(output, efficient_output)
+
+
 @pytest.mark.parametrize(
     "layer_config, expected_type",
     [
@@ -140,7 +217,12 @@ def test_self_attention_weights(dummy_bottleneck_input):
         (FreqCoordConvDownConfig(out_channels=32), FreqCoordConvDownBlock),
         (FreqCoordConvUpConfig(out_channels=32), FreqCoordConvUpBlock),
         (SelfAttentionConfig(attention_channels=32), SelfAttention),
+        (
+            EfficientSelfAttentionConfig(attention_channels=32),
+            EfficientSelfAttention,
+        ),
         (VerticalConvConfig(channels=32), VerticalConv),
+        (VerticalMeanConfig(channels=32), VerticalMean),
     ],
 )
 def test_build_layer_factory(layer_config, expected_type):
diff --git a/tests/test_models/test_bottleneck.py b/tests/test_models/test_bottleneck.py
new file mode 100644
index 0000000..46d9b39
--- /dev/null
+++ b/tests/test_models/test_bottleneck.py
@@ -0,0 +1,55 @@
+import torch
+
+from batdetect2.models.blocks import (
+    SelfAttention,
+    SelfAttentionConfig,
+    VerticalMeanConfig,
+)
+from batdetect2.models.bottleneck import (
+    Bottleneck,
+    BottleneckConfig,
+    build_bottleneck,
+)
+
+
+def test_bottleneck_layers_use_frequency_aggregation_channels() -> None:
+    """Layers after frequency aggregation are built for aggregated channels."""
+    config = BottleneckConfig(
+        channels=128,
+        frequency_aggregation=VerticalMeanConfig(channels=128),
+        layers=[SelfAttentionConfig(attention_channels=32)],
+    )
+
+    bottleneck = build_bottleneck(
+        input_height=8,
+        in_channels=64,
+        config=config,
+    )
+
+    assert isinstance(bottleneck, Bottleneck)
+    attention = bottleneck.layers[0]
+    assert isinstance(attention, SelfAttention)
+    assert attention.in_channels == 128
+
+    output = bottleneck(torch.randn(2, 64, 8, 10))
+
+    assert output.shape == (2, 128, 8, 10)
+
+
+def test_bottleneck_default_frequency_aggregation_matches_channels() -> None:
+    """Minimal configs keep advertised and actual output channels in sync."""
+    config = BottleneckConfig(channels=128, layers=[])
+
+    bottleneck = build_bottleneck(
+        input_height=8,
+        in_channels=64,
+        config=config,
+    )
+
+    assert isinstance(bottleneck, Bottleneck)
+    assert bottleneck.out_channels == 128
+    assert bottleneck.conv_vert.out_channels == 128
+
+    output = bottleneck(torch.randn(2, 64, 8, 10))
+
+    assert output.shape == (2, bottleneck.out_channels, 8, 10)
diff --git a/uv.lock b/uv.lock
index c090df2..4d50e3e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -557,8 +557,8 @@ requires-dist = [
     { name = "soundevent", extras = ["audio", "geometry", "plot"], specifier = ">=2.10.0" },
     { name = "soundfile", specifier = ">=0.12.1" },
     { name = "tensorboard", specifier = ">=2.16.2" },
-    { name = "torch", specifier = ">=1.13.1" },
-    { name = "torchaudio", specifier = ">=1.13.1" },
+    { name = "torch", specifier = ">=2.0.0" },
+    { name = "torchaudio", specifier = ">=2.0.0" },
     { name = "xarray", specifier = ">=2024.0.0" },
 ]
 
@@ -626,10 +626,10 @@ name = "bitsandbytes"
 version = "0.49.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.11' and sys_platform == 'darwin') or (python_full_version < '3.11' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'win32')" },
-    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.11' and sys_platform == 'darwin') or (python_full_version >= '3.11' and sys_platform == 'linux') or (python_full_version >= '3.11' and sys_platform == 'win32')" },
-    { name = "packaging", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
-    { name = "torch", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "packaging" },
+    { name = "torch" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/d8/7d/f1fe0992334b18cd8494f89aeec1dcc674635584fcd9f115784fea3a1d05/bitsandbytes-0.49.2-py3-none-macosx_14_0_arm64.whl", hash = "sha256:87be5975edeac5396d699ecbc39dfc47cf2c026daaf2d5852a94368611a6823f", size = 131940, upload-time = "2026-02-16T21:26:04.572Z" },
@@ -1070,7 +1070,7 @@ resolution-markers = [
     "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" } },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130, upload-time = "2025-04-15T17:47:53.79Z" }
 wheels = [
@@ -1151,7 +1151,7 @@ resolution-markers = [
     "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 dependencies = [
-    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
+    { name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" } },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
 wheels = [
@@ -1345,7 +1345,7 @@ name = "cuda-bindings"
 version = "13.2.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "cuda-pathfinder" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/1a/fe/7351d7e586a8b4c9f89731bfe4cf0148223e8f9903ff09571f78b3fb0682/cuda_bindings-13.2.0-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:08b395f79cb89ce0cd8effff07c4a1e20101b873c256a1aeb286e8fd7bd0f556", size = 5744254, upload-time = "2026-03-11T00:12:29.798Z" },
@@ -1376,37 +1376,37 @@ wheels = [
 
 [package.optional-dependencies]
 cublas = [
-    { name = "nvidia-cublas", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cublas" },
 ]
 cudart = [
-    { name = "nvidia-cuda-runtime", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cuda-runtime" },
 ]
 cufft = [
-    { name = "nvidia-cufft", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cufft" },
 ]
 cufile = [
-    { name = "nvidia-cufile", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cufile" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cuda-cupti" },
 ]
 curand = [
-    { name = "nvidia-curand", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-curand" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cusolver" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cusparse" },
 ]
 nvjitlink = [
-    { name = "nvidia-nvjitlink", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cuda-nvrtc" },
 ]
 nvtx = [
-    { name = "nvidia-nvtx", marker = "(platform_machine != 'ARM64' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-nvtx" },
 ]
 
 [[package]]
@@ -1875,7 +1875,7 @@ name = "exceptiongroup"
 version = "1.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" }
 wheels = [
@@ -2417,7 +2417,7 @@ name = "gunicorn"
 version = "25.3.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "packaging", marker = "platform_machine != 'ARM64' or sys_platform != 'win32'" },
+    { name = "packaging" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/c4/f4/e78fa054248fab913e2eab0332c6c2cb07421fca1ce56d8fe43b6aef57a4/gunicorn-25.3.0.tar.gz", hash = "sha256:f74e1b2f9f76f6cd1ca01198968bd2dd65830edc24b6e8e4d78de8320e2fe889", size = 634883, upload-time = "2026-03-27T00:00:26.092Z" }
 wheels = [
@@ -2638,17 +2638,17 @@ resolution-markers = [
     "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 dependencies = [
-    { name = "colorama", marker = "python_full_version < '3.11' and sys_platform == 'win32'" },
-    { name = "decorator", marker = "python_full_version < '3.11'" },
-    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
-    { name = "jedi", marker = "python_full_version < '3.11'" },
-    { name = "matplotlib-inline", marker = "python_full_version < '3.11'" },
-    { name = "pexpect", marker = "python_full_version < '3.11' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "prompt-toolkit", marker = "python_full_version < '3.11'" },
-    { name = "pygments", marker = "python_full_version < '3.11'" },
-    { name = "stack-data", marker = "python_full_version < '3.11'" },
-    { name = "traitlets", marker = "python_full_version < '3.11'" },
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+    { name = "decorator" },
+    { name = "exceptiongroup" },
+    { name = "jedi" },
+    { name = "matplotlib-inline" },
+    { name = "pexpect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "prompt-toolkit" },
+    { name = "pygments" },
+    { name = "stack-data" },
+    { name = "traitlets" },
+    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/40/18/f8598d287006885e7136451fdea0755af4ebcbfe342836f24deefaed1164/ipython-8.39.0.tar.gz", hash = "sha256:4110ae96012c379b8b6db898a07e186c40a2a1ef5d57a7fa83166047d9da7624", size = 5513971, upload-time = "2026-03-27T10:02:13.94Z" }
 wheels = [
@@ -2674,18 +2674,18 @@ resolution-markers = [
     "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32'",
 ]
 dependencies = [
-    { name = "colorama", marker = "python_full_version >= '3.11' and sys_platform == 'win32'" },
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