diff --git a/docs/docs/metrics/write-your-own.md b/docs/docs/metrics/write-your-own.md
index 65f96b2be..0b87cbb52 100644
--- a/docs/docs/metrics/write-your-own.md
+++ b/docs/docs/metrics/write-your-own.md
@@ -28,8 +28,7 @@ Your implementation should call `writer.write(<object_score>, <object>)` for eve
 from loguru import logger
 
 from encord_active.lib.common.iterator import Iterator
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import AnnotationType, DataType, Metric, MetricType
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
diff --git a/docs/docs/sdk/initialize-a-project.mdx b/docs/docs/sdk/initialize-a-project.mdx
index 834a9bee4..45fb3e956 100644
--- a/docs/docs/sdk/initialize-a-project.mdx
+++ b/docs/docs/sdk/initialize-a-project.mdx
@@ -23,7 +23,7 @@ from pathlib import Path
 from typing import List
 
 from encord_active.lib.metrics.execute import run_metrics
-from encord_active.lib.metrics.types import EmbeddingType
+from encord_active.lib.metrics.metric import EmbeddingType
 from encord_active.lib.project.local import ProjectExistsError, init_local_project
 
 # 1. Choose images to import
@@ -88,7 +88,7 @@ from pathlib import Path
 from typing import List
 
 from encord_active.lib.metrics.execute import run_metrics
-from encord_active.lib.metrics.types import EmbeddingType
+from encord_active.lib.metrics.metric import EmbeddingType
 from encord_active.lib.project.local import ProjectExistsError, init_local_project
 
 # 1. Choose images and label files to import
diff --git a/docs/docs/sdk/run-metrics.md b/docs/docs/sdk/run-metrics.md
index 218ba51e7..259a24b21 100644
--- a/docs/docs/sdk/run-metrics.md
+++ b/docs/docs/sdk/run-metrics.md
@@ -45,7 +45,7 @@ There is a utility function you can use to run targeted subsets of metrics:
 from encord_active.lib.metrics.execute import (
     run_metrics_by_embedding_type,
 )
-from encord_active.lib.metrics.types import EmbeddingType
+from encord_active.lib.metrics.metric import EmbeddingType
 
 run_metrics_by_embedding_type(EmbeddingType.IMAGE, **options)
 run_metrics_by_embedding_type(EmbeddingType.OBJECT, **options)
diff --git a/examples/building-a-custom-metric-function.ipynb b/examples/building-a-custom-metric-function.ipynb
index 8a1494376..288bf80ca 100644
--- a/examples/building-a-custom-metric-function.ipynb
+++ b/examples/building-a-custom-metric-function.ipynb
@@ -104,8 +104,7 @@
     "from typing import List, Optional, Union\n",
     "\n",
     "from encord_active.lib.common.iterator import Iterator\n",
-    "from encord_active.lib.metrics.metric import Metric\n",
-    "from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType\n",
+    "from encord_active.lib.metrics.metric import AnnotationType, DataType, MetricType, Metric\n",
     "from encord_active.lib.metrics.writer import CSVMetricWriter\n",
     "\n",
     "class ExampleMetric(Metric):\n",
@@ -443,8 +442,7 @@
     "import numpy as np\n",
     "from encord_active.lib.common import utils\n",
     "from encord_active.lib.common.iterator import Iterator\n",
-    "from encord_active.lib.metrics.metric import Metric\n",
-    "from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType\n",
+    "from encord_active.lib.metrics.metric import AnnotationType, DataType, Metric, MetricType\n",
     "from encord_active.lib.metrics.writer import CSVMetricWriter\n",
     "from loguru import logger\n",
     "\n",
diff --git a/poetry.lock b/poetry.lock
index dddae515e..c431c7045 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -292,31 +292,6 @@ python-versions = ">=3.7"
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
-[[package]]
-name = "clip"
-version = "1.0"
-description = ""
-category = "main"
-optional = false
-python-versions = "*"
-develop = false
-
-[package.dependencies]
-ftfy = "*"
-regex = "*"
-torch = "*"
-torchvision = "*"
-tqdm = "*"
-
-[package.extras]
-dev = ["pytest"]
-
-[package.source]
-type = "git"
-url = "https://github.com/openai/CLIP.git"
-reference = "HEAD"
-resolved_reference = "a9b1bf5920416aaeaec965c25dd9e8f98c864f16"
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -525,7 +500,7 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
 name = "filelock"
 version = "3.12.0"
 description = "A platform independent file lock."
-category = "main"
+category = "dev"
 optional = false
 python-versions = ">=3.7"
 
@@ -563,17 +538,6 @@ category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4"
 
-[[package]]
-name = "ftfy"
-version = "6.1.1"
-description = "Fixes mojibake and other problems with Unicode, after the fact"
-category = "main"
-optional = false
-python-versions = ">=3.7,<4"
-
-[package.dependencies]
-wcwidth = ">=0.2.5"
-
 [[package]]
 name = "gitdb"
 version = "4.0.10"
@@ -1287,20 +1251,6 @@ category = "dev"
 optional = false
 python-versions = ">=3.7"
 
-[[package]]
-name = "mpmath"
-version = "1.3.0"
-description = "Python library for arbitrary-precision floating-point arithmetic"
-category = "main"
-optional = false
-python-versions = "*"
-
-[package.extras]
-develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
-docs = ["sphinx"]
-gmpy = ["gmpy2 (>=2.1.0a4)"]
-tests = ["pytest (>=4.6)"]
-
 [[package]]
 name = "mypy"
 version = "0.981"
@@ -1451,21 +1401,6 @@ category = "main"
 optional = false
 python-versions = ">=3.5"
 
-[[package]]
-name = "networkx"
-version = "3.1"
-description = "Python package for creating and manipulating graphs and networks"
-category = "main"
-optional = false
-python-versions = ">=3.8"
-
-[package.extras]
-default = ["matplotlib (>=3.4)", "numpy (>=1.20)", "pandas (>=1.3)", "scipy (>=1.8)"]
-developer = ["mypy (>=1.1)", "pre-commit (>=3.2)"]
-doc = ["nb2plots (>=0.6)", "numpydoc (>=1.5)", "pillow (>=9.4)", "pydata-sphinx-theme (>=0.13)", "sphinx (>=6.1)", "sphinx-gallery (>=0.12)", "texext (>=0.6.7)"]
-extra = ["lxml (>=4.6)", "pydot (>=1.4.2)", "pygraphviz (>=1.10)", "sympy (>=1.10)"]
-test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"]
-
 [[package]]
 name = "nodeenv"
 version = "1.7.0"
@@ -2170,14 +2105,6 @@ packaging = "*"
 [package.extras]
 test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"]
 
-[[package]]
-name = "regex"
-version = "2023.5.5"
-description = "Alternative regular expression module, to replace re."
-category = "main"
-optional = false
-python-versions = ">=3.6"
-
 [[package]]
 name = "requests"
 version = "2.29.0"
@@ -2467,17 +2394,6 @@ python-versions = ">=3.6"
 plotly = ">=4.14.3"
 streamlit = ">=0.63"
 
-[[package]]
-name = "sympy"
-version = "1.12"
-description = "Computer algebra system (CAS) in Python"
-category = "main"
-optional = false
-python-versions = ">=3.8"
-
-[package.dependencies]
-mpmath = ">=0.19"
-
 [[package]]
 name = "tabulate"
 version = "0.9.0"
@@ -2585,35 +2501,29 @@ python-versions = ">=3.5"
 
 [[package]]
 name = "torch"
-version = "2.0.1"
+version = "1.12.1"
 description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
 category = "main"
 optional = false
-python-versions = ">=3.8.0"
+python-versions = ">=3.7.0"
 
 [package.dependencies]
-filelock = "*"
-jinja2 = "*"
-networkx = "*"
-sympy = "*"
 typing-extensions = "*"
 
-[package.extras]
-opt-einsum = ["opt-einsum (>=3.3)"]
-
 [[package]]
 name = "torchvision"
-version = "0.15.2"
+version = "0.13.1"
 description = "image and video datasets and models for torch deep learning"
 category = "main"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 
 [package.dependencies]
 numpy = "*"
 pillow = ">=5.3.0,<8.3.0 || >=8.4.0"
 requests = "*"
-torch = "2.0.1"
+torch = "1.12.1"
+typing-extensions = "*"
 
 [package.extras]
 scipy = ["scipy"]
@@ -3025,7 +2935,7 @@ notebooks = ["jupyterlab", "ipywidgets"]
 [metadata]
 lock-version = "1.1"
 python-versions = ">=3.9,<3.9.7 || >3.9.7,<3.11"
-content-hash = "ede337f6b5df598e4a61e704dd37d2cdfd160571836a78c17dd400ffa90ba82f"
+content-hash = "9bf7547aa0f400b04b14f3f80026d7fad7f8843ea9591a54a2a24633b5b9d1cf"
 
 [metadata.files]
 aiofiles = [
@@ -3327,7 +3237,6 @@ click = [
     {file = "click-8.1.3-py3-none-any.whl", hash = "sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48"},
     {file = "click-8.1.3.tar.gz", hash = "sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e"},
 ]
-clip = []
 colorama = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
@@ -3521,10 +3430,6 @@ fqdn = [
     {file = "fqdn-1.5.1-py3-none-any.whl", hash = "sha256:3a179af3761e4df6eb2e026ff9e1a3033d3587bf980a0b1b2e1e5d08d7358014"},
     {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"},
 ]
-ftfy = [
-    {file = "ftfy-6.1.1-py3-none-any.whl", hash = "sha256:0ffd33fce16b54cccaec78d6ec73d95ad370e5df5a25255c8966a6147bd667ca"},
-    {file = "ftfy-6.1.1.tar.gz", hash = "sha256:bfc2019f84fcd851419152320a6375604a0f1459c281b5b199b2cd0d2e727f8f"},
-]
 gitdb = [
     {file = "gitdb-4.0.10-py3-none-any.whl", hash = "sha256:c286cf298426064079ed96a9e4a9d39e7f3e9bf15ba60701e95f5492f28415c7"},
     {file = "gitdb-4.0.10.tar.gz", hash = "sha256:6eb990b69df4e15bad899ea868dc46572c3f75339735663b81de79b06f17eb9a"},
@@ -3970,10 +3875,6 @@ more-itertools = [
     {file = "more-itertools-9.1.0.tar.gz", hash = "sha256:cabaa341ad0389ea83c17a94566a53ae4c9d07349861ecb14dc6d0345cf9ac5d"},
     {file = "more_itertools-9.1.0-py3-none-any.whl", hash = "sha256:d2bc7f02446e86a68911e58ded76d6561eea00cddfb2a91e7019bbb586c799f3"},
 ]
-mpmath = [
-    {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
-    {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
-]
 mypy = [
     {file = "mypy-0.981-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:4bc460e43b7785f78862dab78674e62ec3cd523485baecfdf81a555ed29ecfa0"},
     {file = "mypy-0.981-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:756fad8b263b3ba39e4e204ee53042671b660c36c9017412b43af210ddee7b08"},
@@ -4028,10 +3929,6 @@ nest-asyncio = [
     {file = "nest_asyncio-1.5.6-py3-none-any.whl", hash = "sha256:b9a953fb40dceaa587d109609098db21900182b16440652454a146cffb06e8b8"},
     {file = "nest_asyncio-1.5.6.tar.gz", hash = "sha256:d267cc1ff794403f7df692964d1d2a3fa9418ffea2a3f6859a439ff482fef290"},
 ]
-networkx = [
-    {file = "networkx-3.1-py3-none-any.whl", hash = "sha256:4f33f68cb2afcf86f28a45f43efc27a9386b535d567d2127f8f61d51dec58d36"},
-    {file = "networkx-3.1.tar.gz", hash = "sha256:de346335408f84de0eada6ff9fafafff9bcda11f0a0dfaa931133debb146ab61"},
-]
 nodeenv = [
     {file = "nodeenv-1.7.0-py2.py3-none-any.whl", hash = "sha256:27083a7b96a25f2f5e1d8cb4b6317ee8aeda3bdd121394e5ac54e498028a042e"},
     {file = "nodeenv-1.7.0.tar.gz", hash = "sha256:e0e7f7dfb85fc5394c6fe1e8fa98131a2473e04311a45afb6508f7cf1836fa2b"},
@@ -4648,96 +4545,6 @@ qtpy = [
     {file = "QtPy-2.3.1-py3-none-any.whl", hash = "sha256:5193d20e0b16e4d9d3bc2c642d04d9f4e2c892590bd1b9c92bfe38a95d5a2e12"},
     {file = "QtPy-2.3.1.tar.gz", hash = "sha256:a8c74982d6d172ce124d80cafd39653df78989683f760f2281ba91a6e7b9de8b"},
 ]
-regex = [
-    {file = "regex-2023.5.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:48c9ec56579d4ba1c88f42302194b8ae2350265cb60c64b7b9a88dcb7fbde309"},
-    {file = "regex-2023.5.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:02f4541550459c08fdd6f97aa4e24c6f1932eec780d58a2faa2068253df7d6ff"},
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 ]
 torchvision = [
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-    {file = "torchvision-0.15.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:63df26673e66cba3f17e07c327a8cafa3cce98265dbc3da329f1951d45966838"},
-    {file = "torchvision-0.15.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:b85f98d4cc2f72452f6792ab4463a3541bc5678a8cdd3da0e139ba2fe8b56d42"},
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+    {file = "torchvision-0.13.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:19286a733c69dcbd417b86793df807bd227db5786ed787c17297741a9b0d0fc7"},
+    {file = "torchvision-0.13.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:08f592ea61836ebeceb5c97f4d7a813b9d7dc651bbf7ce4401563ccfae6a21fc"},
+    {file = "torchvision-0.13.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:ef5fe3ec1848123cd0ec74c07658192b3147dcd38e507308c790d5943e87b88c"},
+    {file = "torchvision-0.13.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:099874088df104d54d8008f2a28539ca0117b512daed8bf3c2bbfa2b7ccb187a"},
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+    {file = "torchvision-0.13.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:b167934a5943242da7b1e59318f911d2d253feeca0d13ad5d832b58eed943401"},
+    {file = "torchvision-0.13.1-cp39-cp39-win_amd64.whl", hash = "sha256:0e77706cc90462653620e336bb90daf03d7bf1b88c3a9a3037df8d111823a56e"},
 ]
 tornado = [
     {file = "tornado-6.3.1-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:db181eb3df8738613ff0a26f49e1b394aade05034b01200a63e9662f347d4415"},
diff --git a/pyproject.toml b/pyproject.toml
index 8637c9c2b..65c142164 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,39 +3,31 @@ name = "encord-active"
 version = "v0.1.58"
 description = "Enable users to improve machine learning models in an active learning fashion via data, label, and model quality."
 authors = ["Cord Technologies Limited <hello@encord.com>"]
-classifiers = [
-  "Environment :: Console",
-  "Environment :: Web Environment",
-  "Intended Audience :: Developers",
-  "Intended Audience :: Education",
-  "Intended Audience :: Information Technology",
-  "Intended Audience :: Science/Research",
-  "Operating System :: OS Independent",
-  "Topic :: Scientific/Engineering",
-  "Topic :: Scientific/Engineering :: Artificial Intelligence",
-  "Topic :: Scientific/Engineering :: Information Analysis",
-  "Topic :: Software Development",
-  "Topic :: Software Development :: Quality Assurance",
+classifiers=[
+    "Environment :: Console",
+    "Environment :: Web Environment",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Education",
+    "Intended Audience :: Information Technology",
+    "Intended Audience :: Science/Research",
+    "Operating System :: OS Independent",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Information Analysis",
+    "Topic :: Software Development",
+    "Topic :: Software Development :: Quality Assurance"
 ]
 documentation = "https://docs.encord.com/active/docs"
 homepage = "https://encord.com/encord-active/"
-keywords = [
-  "encord",
-  "active",
-  "machine",
-  "learning",
-  "data",
-  "label",
-  "model",
-  "quality",
-  "test",
-]
+keywords = ["encord", "active", "machine", "learning", "data", "label", "model", "quality", "test"]
 readme = "README.md"
 repository = "https://github.com/encord-team/encord-active"
 
 include = ['.env']
 
-packages = [{ include = "encord_active", from = "src" }]
+packages = [
+    { include = "encord_active", from = "src" },
+]
 
 license = "Apache-2.0"
 
@@ -52,6 +44,8 @@ natsort = "^8.1.0"
 pandas = "^1.4.3"
 shapely = "^1.7.0"
 watchdog = "^2.1.9"
+torch = "^1.12.1"
+torchvision = "^0.13.1"
 faiss-cpu = "^1.7.2"
 matplotlib = "^3.5.3"
 scikit-learn = "^1.0.1"
@@ -69,28 +63,23 @@ rich = "^12.6.0"
 PyYAML = "^6.0"
 toml = "^0.10.2"
 pydantic = "^1.10.2"
-pycocotools = { version = "^2.0.6", optional = true }
+pycocotools = {version = "^2.0.6", optional = true}
 psutil = "^5.9.4"
 pandera = "^0.13.4"
-jupyterlab = { version = "^3.5.2", optional = true }
-ipywidgets = { version = "^8.0.4", optional = true }
+jupyterlab = {version = "^3.5.2", optional = true}
+ipywidgets = {version = "^8.0.4", optional = true}
 inquirerpy = "^0.3.4"
 statsmodels = "^0.13.5"
 umap-learn = "^0.5.3"
 streamlit-plotly-events = "^0.0.6"
 encord-active-components = "^0.0.12"
-llvmlite = "^0.39.1"                                     # Pinning, as lower versions conflict with other libs
+llvmlite = "^0.39.1" # Pinning, as lower versions conflict with other libs
 gitpython = "^3.1.31"
 prisma = "^0.8.2"
 fastapi = "^0.95.0"
-uvicorn = { extras = ["standard"], version = "^0.21.1" }
+uvicorn = {extras = ["standard"], version = "^0.21.1"}
 nodejs-bin = "^18.4.0a4"
 pyjwt = "^2.7.0"
-torch = "^2.0.0"
-clip = { git = "https://github.com/openai/CLIP.git" }
-torchvision = "^0.15.2"
-ftfy = "^6.1.1"
-regex = "^2023.5.5"
 
 [tool.poetry.extras]
 coco = ["pycocotools"]
diff --git a/src/encord_active/app/common/state.py b/src/encord_active/app/common/state.py
index eb91d60fb..a21a86930 100644
--- a/src/encord_active/app/common/state.py
+++ b/src/encord_active/app/common/state.py
@@ -13,7 +13,7 @@
 from encord_active.lib.db.connection import DBConnection
 from encord_active.lib.db.merged_metrics import MergedMetrics, initialize_merged_metrics
 from encord_active.lib.embeddings.utils import Embedding2DSchema
-from encord_active.lib.metrics.types import EmbeddingType
+from encord_active.lib.metrics.metric import EmbeddingType
 from encord_active.lib.metrics.utils import MetricData, MetricSchema
 from encord_active.lib.model_predictions.reader import LabelSchema, OntologyObjectJSON
 from encord_active.lib.model_predictions.writer import OntologyClassificationJSON
diff --git a/src/encord_active/app/label_onboarding/label_onboarding.py b/src/encord_active/app/label_onboarding/label_onboarding.py
index 90e4329a4..a49b29cce 100644
--- a/src/encord_active/app/label_onboarding/label_onboarding.py
+++ b/src/encord_active/app/label_onboarding/label_onboarding.py
@@ -14,7 +14,7 @@
     execute_metrics,
     get_metrics_by_embedding_type,
 )
-from encord_active.lib.metrics.types import EmbeddingType
+from encord_active.lib.metrics.metric import EmbeddingType
 from encord_active.lib.project.project import Project
 
 
diff --git a/src/encord_active/app/model_quality/prediction_types/classification_type_builder.py b/src/encord_active/app/model_quality/prediction_types/classification_type_builder.py
index 8be71c2e5..cd66770cd 100644
--- a/src/encord_active/app/model_quality/prediction_types/classification_type_builder.py
+++ b/src/encord_active/app/model_quality/prediction_types/classification_type_builder.py
@@ -28,7 +28,7 @@
 from encord_active.lib.charts.scopes import PredictionMatchScope
 from encord_active.lib.embeddings.dimensionality_reduction import get_2d_embedding_data
 from encord_active.lib.embeddings.utils import Embedding2DSchema
-from encord_active.lib.metrics.types import EmbeddingType
+from encord_active.lib.metrics.metric import EmbeddingType
 from encord_active.lib.metrics.utils import MetricSchema
 from encord_active.lib.model_predictions.classification_metrics import (
     match_predictions_and_labels,
@@ -253,7 +253,7 @@ def render_explorer(self):
 
         if EmbeddingType.IMAGE not in get_state().reduced_embeddings:
             get_state().reduced_embeddings[EmbeddingType.IMAGE] = get_2d_embedding_data(
-                get_state().project_paths, EmbeddingType.IMAGE
+                get_state().project_paths.embeddings, EmbeddingType.IMAGE
             )
 
         metric_name = get_state().predictions.metric_datas_classification.selected_prediction
diff --git a/src/encord_active/app/projects_page.py b/src/encord_active/app/projects_page.py
index 9e75ec817..013b437f1 100644
--- a/src/encord_active/app/projects_page.py
+++ b/src/encord_active/app/projects_page.py
@@ -21,7 +21,7 @@
     try_find_parent_project,
 )
 from encord_active.lib.common.image_utils import show_image_and_draw_polygons
-from encord_active.lib.metrics.types import AnnotationType
+from encord_active.lib.metrics.metric import AnnotationType
 from encord_active.lib.metrics.utils import load_metric_metadata
 from encord_active.lib.model_predictions.writer import (
     iterate_classification_attribute_options,
diff --git a/src/encord_active/cli/main.py b/src/encord_active/cli/main.py
index 9e80c36d5..b27df9376 100644
--- a/src/encord_active/cli/main.py
+++ b/src/encord_active/cli/main.py
@@ -205,7 +205,7 @@ def import_local_project(
         run_metrics_by_embedding_type,
     )
     from encord_active.lib.metrics.heuristic.img_features import AreaMetric
-    from encord_active.lib.metrics.types import EmbeddingType
+    from encord_active.lib.metrics.metric import EmbeddingType
     from encord_active.lib.project.local import (
         NoFilesFoundError,
         ProjectExistsError,
diff --git a/src/encord_active/lib/db/merged_metrics.py b/src/encord_active/lib/db/merged_metrics.py
index 9a325575d..341b7dea9 100644
--- a/src/encord_active/lib/db/merged_metrics.py
+++ b/src/encord_active/lib/db/merged_metrics.py
@@ -8,7 +8,7 @@
 from encord_active.lib.db.connection import DBConnection
 from encord_active.lib.db.tags import Tag, TagScope
 from encord_active.lib.labels.classification import ClassificationType
-from encord_active.lib.metrics.types import DataType, EmbeddingType
+from encord_active.lib.metrics.metric import DataType, EmbeddingType
 from encord_active.lib.metrics.utils import load_metric_metadata
 from encord_active.lib.project.project_file_structure import ProjectFileStructure
 
diff --git a/src/encord_active/lib/embeddings/embeddings.py b/src/encord_active/lib/embeddings/cnn.py
similarity index 56%
rename from src/encord_active/lib/embeddings/embeddings.py
rename to src/encord_active/lib/embeddings/cnn.py
index b999e0b00..bcd04858f 100644
--- a/src/encord_active/lib/embeddings/embeddings.py
+++ b/src/encord_active/lib/embeddings/cnn.py
@@ -1,37 +1,71 @@
+import logging
+import os
 import pickle
 import time
 from pathlib import Path
-from typing import List, Optional
+from typing import List, Optional, Tuple
 
 import numpy as np
 import torch
+import torchvision.transforms as torch_transforms
 from encord.objects.common import PropertyType
 from encord.project_ontology.object_type import ObjectShape
-from loguru import logger
 from PIL import Image
+from torch import nn
+from torchvision.models import EfficientNet_V2_S_Weights, efficientnet_v2_s
+from torchvision.models.feature_extraction import create_feature_extractor
 
 from encord_active.lib.common.iterator import Iterator
 from encord_active.lib.common.utils import get_bbox_from_encord_label_object
-from encord_active.lib.embeddings.models.clip_embedder import CLIPEmbedder
-from encord_active.lib.embeddings.models.embedder_model import ImageEmbedder
-from encord_active.lib.embeddings.utils import ClassificationAnswer, LabelEmbedding
-from encord_active.lib.metrics.types import EmbeddingType
-from encord_active.lib.project.project_file_structure import ProjectFileStructure
+from encord_active.lib.embeddings.utils import (
+    EMBEDDING_TYPE_TO_FILENAME,
+    ClassificationAnswer,
+    LabelEmbedding,
+)
+from encord_active.lib.metrics.metric import EmbeddingType
 
+logger = logging.getLogger(__name__)
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-def get_default_embedder() -> ImageEmbedder:
-    return CLIPEmbedder()
 
+def get_model_and_transforms() -> Tuple[nn.Module, nn.Module]:
+    weights = EfficientNet_V2_S_Weights.DEFAULT
+    model = efficientnet_v2_s(weights=weights).to(DEVICE)
+    embedding_extractor = create_feature_extractor(model, return_nodes={"avgpool": "my_avgpool"})
+    for p in embedding_extractor.parameters():
+        p.requires_grad = False
+    embedding_extractor.eval()
+    return embedding_extractor, weights.transforms()
 
-def assemble_object_batch(data_unit: dict, img_path: Path) -> List[Image.Image]:
-    try:
-        image = np.asarray(Image.open(img_path).convert("RGB"))
-    except OSError:
-        logger.warning(f"Image with path {img_path} seems to be broken. Skipping.")
-        return []
 
-    img_h, img_w, *_ = image.shape
-    img_batch: List[Image.Image] = []
+def adjust_image_channels(image: torch.Tensor) -> torch.Tensor:
+    if image.shape[0] == 4:
+        image = image[:3]
+    elif image.shape[0] < 3:
+        image = image.repeat(3, 1, 1)
+
+    return image
+
+
+def image_path_to_tensor(image_path: Path) -> torch.Tensor:
+    image = Image.open(image_path.as_posix())
+    transform = torch_transforms.ToTensor()
+    image = transform(image)
+
+    image = adjust_image_channels(image)
+
+    return image
+
+
+def assemble_object_batch(data_unit: dict, img_path: Path, transforms: Optional[nn.Module]):
+    if transforms is None:
+        transforms = torch.nn.Sequential()
+
+    try:
+        image = image_path_to_tensor(img_path)
+    except Exception:
+        return None
+    img_batch: List[torch.Tensor] = []
 
     for obj in data_unit["labels"].get("objects", []):
         if obj["shape"] in [
@@ -42,8 +76,8 @@ def assemble_object_batch(data_unit: dict, img_path: Path) -> List[Image.Image]:
             try:
                 out = get_bbox_from_encord_label_object(
                     obj,
-                    w=img_w,
-                    h=img_h,
+                    image.shape[2],
+                    image.shape[1],
                 )
 
                 if out is None:
@@ -51,55 +85,25 @@ def assemble_object_batch(data_unit: dict, img_path: Path) -> List[Image.Image]:
                 x, y, w, h = out
 
                 img_patch = image[:, y : y + h, x : x + w]
-                img_batch.append(Image.fromarray(img_patch))
+                img_batch.append(transforms(img_patch))
             except Exception as e:
                 logger.warning(f"Error with object {obj['objectHash']}: {e}")
                 continue
-    return img_batch
+    return torch.stack(img_batch).to(DEVICE) if len(img_batch) > 0 else None
 
 
 @torch.inference_mode()
-def generate_image_embeddings(
-    iterator: Iterator, feature_extractor: Optional[ImageEmbedder] = None, batch_size=100
-) -> List[LabelEmbedding]:
+def generate_cnn_image_embeddings(iterator: Iterator) -> List[LabelEmbedding]:
     start = time.perf_counter()
-    if feature_extractor is None:
-        feature_extractor = get_default_embedder()
-
-    raw_embeddings: list[np.ndarray] = []
-    batch = []
-    skip: set[int] = set()
-    for i, (data_unit, img_pth) in enumerate(iterator.iterate(desc="Embedding image data.")):
-        if img_pth is None:
-            skip.add(i)
-            continue
-        try:
-            batch.append(Image.open(img_pth).convert("RGB"))
-        except OSError:
-            logger.warning(f"Image with path {img_pth} seems to be broken. Skipping.")
-            skip.add(i)
-            continue
-
-        if len(batch) >= batch_size:
-            raw_embeddings.append(feature_extractor.embed_images(batch))
-            batch = []
-
-    if batch:
-        raw_embeddings.append(feature_extractor.embed_images(batch))
-
-    if len(raw_embeddings) > 1:
-        raw_np_embeddings = np.concatenate(raw_embeddings)
-    else:
-        raw_np_embeddings = raw_embeddings[0]
+    feature_extractor, transforms = get_model_and_transforms()
 
     collections: List[LabelEmbedding] = []
-    offset = 0
-    for i, (data_unit, img_pth) in enumerate(iterator.iterate(desc="Storing embeddings.")):
-        if i in skip:
-            offset += 1
+    for data_unit, img_pth in iterator.iterate(desc="Embedding image data."):
+        embedding = get_embdding_for_image(feature_extractor, transforms, img_pth)
+
+        if embedding is None:
             continue
 
-        embedding = raw_np_embeddings[i - offset]
         entry = LabelEmbedding(
             url=data_unit["data_link"],
             label_row=iterator.label_hash,
@@ -123,48 +127,45 @@ def generate_image_embeddings(
 
 
 @torch.inference_mode()
-def generate_object_embeddings(
-    iterator: Iterator, feature_extractor: Optional[ImageEmbedder] = None
-) -> List[LabelEmbedding]:
+def generate_cnn_object_embeddings(iterator: Iterator) -> List[LabelEmbedding]:
     start = time.perf_counter()
-    if feature_extractor is None:
-        feature_extractor = get_default_embedder()
+    feature_extractor, transforms = get_model_and_transforms()
 
     collections: List[LabelEmbedding] = []
     for data_unit, img_pth in iterator.iterate(desc="Embedding object data."):
         if img_pth is None:
             continue
 
-        batch = assemble_object_batch(data_unit, img_pth)
-        if not batch:
+        batches = assemble_object_batch(data_unit, img_pth, transforms=transforms)
+        if batches is None:
             continue
 
-        embeddings = feature_extractor.embed_images(batch)
-        for obj, emb in zip(data_unit["labels"].get("objects", []), embeddings):
-            if obj["shape"] not in [
+        embeddings = feature_extractor(batches)["my_avgpool"]
+        embeddings_torch = torch.flatten(embeddings, start_dim=1).cpu().detach().numpy()
+
+        for obj, emb in zip(data_unit["labels"].get("objects", []), embeddings_torch):
+            if obj["shape"] in [
                 ObjectShape.POLYGON.value,
                 ObjectShape.BOUNDING_BOX.value,
                 ObjectShape.ROTATABLE_BOUNDING_BOX.value,
             ]:
-                continue
-
-            last_edited_by = obj["lastEditedBy"] if "lastEditedBy" in obj.keys() else obj["createdBy"]
-
-            entry = LabelEmbedding(
-                url=data_unit["data_link"],
-                label_row=iterator.label_hash,
-                data_unit=data_unit["data_hash"],
-                frame=iterator.frame,
-                labelHash=obj["objectHash"],
-                lastEditedBy=last_edited_by,
-                featureHash=obj["featureHash"],
-                name=obj["name"],
-                dataset_title=iterator.dataset_title,
-                embedding=emb,
-                classification_answers=None,
-            )
+                last_edited_by = obj["lastEditedBy"] if "lastEditedBy" in obj.keys() else obj["createdBy"]
+
+                entry = LabelEmbedding(
+                    url=data_unit["data_link"],
+                    label_row=iterator.label_hash,
+                    data_unit=data_unit["data_hash"],
+                    frame=iterator.frame,
+                    labelHash=obj["objectHash"],
+                    lastEditedBy=last_edited_by,
+                    featureHash=obj["featureHash"],
+                    name=obj["name"],
+                    dataset_title=iterator.dataset_title,
+                    embedding=emb,
+                    classification_answers=None,
+                )
 
-            collections.append(entry)
+                collections.append(entry)
 
     logger.info(
         f"Generating {len(iterator)} embeddings took {str(time.perf_counter() - start)} seconds",
@@ -174,10 +175,8 @@ def generate_object_embeddings(
 
 
 @torch.inference_mode()
-def generate_classification_embeddings(
-    iterator: Iterator, feature_extractor: Optional[ImageEmbedder]
-) -> List[LabelEmbedding]:
-    image_collections = get_embeddings(iterator, embedding_type=EmbeddingType.IMAGE)
+def generate_cnn_classification_embeddings(iterator: Iterator) -> List[LabelEmbedding]:
+    image_collections = get_cnn_embeddings(iterator, embedding_type=EmbeddingType.IMAGE)
 
     ontology_class_hash_to_index: dict[str, dict] = {}
     ontology_class_hash_to_question_hash: dict[str, str] = {}
@@ -190,18 +189,14 @@ def generate_classification_embeddings(
             ontology_class_hash = class_label.feature_node_hash
             ontology_class_hash_to_index[ontology_class_hash] = {}
             ontology_class_hash_to_question_hash[ontology_class_hash] = class_question.feature_node_hash
-            for index, option in enumerate(class_question.options):  # type: ignore
+            for index, option in enumerate(class_question.options):
                 ontology_class_hash_to_index[ontology_class_hash][option.feature_node_hash] = index
 
     start = time.perf_counter()
-    if feature_extractor is None:
-        feature_extractor = get_default_embedder()
+    feature_extractor, transforms = get_model_and_transforms()
 
     collections = []
     for data_unit, img_pth in iterator.iterate(desc="Embedding classification data."):
-        if img_pth is None:
-            continue
-
         matching_image_collections = [
             collection
             for collection in image_collections
@@ -211,13 +206,7 @@ def generate_classification_embeddings(
         ]
 
         if not matching_image_collections:
-            try:
-                image = Image.open(img_pth).convert("RGB")
-            except OSError:
-                logger.warning(f"Image with path {img_pth} seems to be broken. Skipping.")
-                continue
-
-            embedding = feature_extractor.embed_image(image)
+            embedding = get_embdding_for_image(feature_extractor, transforms, img_pth)
         else:
             embedding = matching_image_collections[0]["embedding"]
 
@@ -276,43 +265,59 @@ def generate_classification_embeddings(
     return collections
 
 
-def get_embeddings(iterator: Iterator, embedding_type: EmbeddingType, *, force: bool = False) -> List[LabelEmbedding]:
+def get_cnn_embeddings(
+    iterator: Iterator, embedding_type: EmbeddingType, *, force: bool = False
+) -> List[LabelEmbedding]:
     if embedding_type not in [EmbeddingType.CLASSIFICATION, EmbeddingType.IMAGE, EmbeddingType.OBJECT]:
-        raise Exception(f"Undefined embedding type '{embedding_type}' for get_embeddings method")
+        raise Exception(f"Undefined embedding type '{embedding_type}' for get_cnn_embeddings method")
 
-    pfs = ProjectFileStructure(iterator.cache_dir)
-    embedding_path = pfs.get_embeddings_file(embedding_type)
+    target_folder = os.path.join(iterator.cache_dir, "embeddings")
+    embedding_path = os.path.join(target_folder, f"{EMBEDDING_TYPE_TO_FILENAME[embedding_type]}")
 
     if force:
         logger.info("Regenerating CNN embeddings...")
-        embeddings = generate_embeddings(iterator, embedding_type, embedding_path)
+        cnn_embeddings = generate_cnn_embeddings(iterator, embedding_type, embedding_path)
     else:
         try:
             with open(embedding_path, "rb") as f:
-                embeddings = pickle.load(f)
+                cnn_embeddings = pickle.load(f)
         except FileNotFoundError:
             logger.info(f"{embedding_path} not found. Generating embeddings...")
-            embeddings = generate_embeddings(iterator, embedding_type, embedding_path)
 
-    return embeddings
+            cnn_embeddings = generate_cnn_embeddings(iterator, embedding_type, embedding_path)
+
+    return cnn_embeddings
 
 
-def generate_embeddings(
-    iterator: Iterator, embedding_type: EmbeddingType, target: Path, feature_extractor: Optional[ImageEmbedder] = None
-):
+def generate_cnn_embeddings(iterator: Iterator, embedding_type: EmbeddingType, target: str):
     if embedding_type == EmbeddingType.IMAGE:
-        embeddings = generate_image_embeddings(iterator, feature_extractor=feature_extractor)
+        cnn_embeddings = generate_cnn_image_embeddings(iterator)
     elif embedding_type == EmbeddingType.OBJECT:
-        embeddings = generate_object_embeddings(iterator, feature_extractor=feature_extractor)
+        cnn_embeddings = generate_cnn_object_embeddings(iterator)
     elif embedding_type == EmbeddingType.CLASSIFICATION:
-        embeddings = generate_classification_embeddings(iterator, feature_extractor=feature_extractor)
+        cnn_embeddings = generate_cnn_classification_embeddings(iterator)
     else:
         raise ValueError(f"Unsupported embedding type {embedding_type}")
 
-    if embeddings:
-        target.parent.mkdir(parents=True, exist_ok=True)
-        target.write_bytes(pickle.dumps(embeddings))
+    if cnn_embeddings:
+        target_path = Path(target)
+        target_path.parent.mkdir(parents=True, exist_ok=True)
+        target_path.write_bytes(pickle.dumps(cnn_embeddings))
 
     logger.info("Done!")
 
-    return embeddings
+    return cnn_embeddings
+
+
+def get_embdding_for_image(feature_extractor, transforms, img_pth: Optional[Path] = None) -> Optional[np.ndarray]:
+    if img_pth is None:
+        return None
+
+    try:
+        image = image_path_to_tensor(img_pth)
+        transformed_image = transforms(image).unsqueeze(0)
+        embedding = feature_extractor(transformed_image.to(DEVICE))["my_avgpool"]
+        return torch.flatten(embedding).cpu().detach().numpy()
+    except:
+        logger.error(f"Falied generating embedding for file: {img_pth}")
+        return None
diff --git a/src/encord_active/lib/embeddings/dimensionality_reduction.py b/src/encord_active/lib/embeddings/dimensionality_reduction.py
index e2c037471..fd3217baf 100644
--- a/src/encord_active/lib/embeddings/dimensionality_reduction.py
+++ b/src/encord_active/lib/embeddings/dimensionality_reduction.py
@@ -9,11 +9,11 @@
 from pandera.typing import DataFrame
 
 from encord_active.lib.embeddings.utils import (
+    EMBEDDING_REDUCED_TO_FILENAME,
     Embedding2DSchema,
     EmbeddingType,
     load_collections,
 )
-from encord_active.lib.project.project_file_structure import ProjectFileStructure
 
 warnings.filterwarnings("ignore", "n_neighbors is larger than the dataset size", category=UserWarning)
 MIN_SAMPLES = 4  # The number 4 is experimentally determined, less than this creates error for UMAP calculation
@@ -23,9 +23,8 @@ def generate_2d_embedding_data(embedding_type: EmbeddingType, project_dir: Path)
     """
     This function transforms high dimensional embedding data to 2D and saves it to a file
     """
-    pfs = ProjectFileStructure(project_dir)
 
-    collections = load_collections(embedding_type, pfs.embeddings)
+    collections = load_collections(embedding_type, project_dir / "embeddings")
     if not collections:
         return
 
@@ -63,14 +62,15 @@ def generate_2d_embedding_data(embedding_type: EmbeddingType, project_dir: Path)
         embeddings_2d_collection["x"].append(embeddings_2d[counter, 0])
         embeddings_2d_collection["y"].append(embeddings_2d[counter, 1])
 
-    target_path = pfs.get_embeddings_file(embedding_type, reduced=True)
+    target_path = Path(project_dir / "embeddings" / EMBEDDING_REDUCED_TO_FILENAME[embedding_type])
     target_path.write_bytes(pickle.dumps(embeddings_2d_collection))
 
 
 def get_2d_embedding_data(
-    project_file_structure: ProjectFileStructure, embedding_type: EmbeddingType
+    embeddings_path: Path, embedding_type: EmbeddingType
 ) -> Optional[DataFrame[Embedding2DSchema]]:
-    embedding_file_path = project_file_structure.get_embeddings_file(embedding_type, reduced=True)
+
+    embedding_file_path = embeddings_path / EMBEDDING_REDUCED_TO_FILENAME[embedding_type]
 
     if not embedding_file_path.exists():
         return None
diff --git a/src/encord_active/lib/embeddings/models/__init__.py b/src/encord_active/lib/embeddings/models/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/src/encord_active/lib/embeddings/models/clip_embedder.py b/src/encord_active/lib/embeddings/models/clip_embedder.py
deleted file mode 100644
index 4171f4229..000000000
--- a/src/encord_active/lib/embeddings/models/clip_embedder.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import clip
-import numpy as np
-import torch
-from PIL import Image
-
-from encord_active.lib.embeddings.models.embedder_model import ImageEmbedder
-
-
-class CLIPEmbedder(ImageEmbedder):
-    def __init__(self):
-        super().__init__(supports_text_embeddings=True)
-        self.model, self.preprocess = clip.load("ViT-B/32", device=self.device)
-
-    def embed_texts(self, texts: list[str]) -> np.ndarray:
-        tokens = torch.stack([clip.tokenize(t) for t in texts])
-        return self.execute_with_largest_batch_size(self.model.encode_text, tokens)
-
-    def embed_images(self, images: list[Image.Image]) -> np.ndarray:
-        tensors = torch.stack([self.preprocess(i) for i in images])  # type: ignore
-        return self.execute_with_largest_batch_size(self.model.encode_image, tensors)
diff --git a/src/encord_active/lib/embeddings/models/embedder_model.py b/src/encord_active/lib/embeddings/models/embedder_model.py
deleted file mode 100644
index f4d6573e4..000000000
--- a/src/encord_active/lib/embeddings/models/embedder_model.py
+++ /dev/null
@@ -1,114 +0,0 @@
-from abc import ABC, abstractmethod
-from typing import Callable
-
-import numpy as np
-import torch
-from PIL import Image
-
-
-class ImageEmbedder(ABC):
-    def __init__(self, supports_text_embeddings: bool = False):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        self.max_batch_size: dict[Callable[[torch.Tensor], torch.Tensor], int] = {}
-        self._supports_text_embeddings = supports_text_embeddings
-
-    @torch.inference_mode()
-    def execute_with_largest_batch_size(
-        self, function: Callable[[torch.Tensor], torch.Tensor], input_: torch.Tensor
-    ) -> np.ndarray:
-        """
-        Tries to utillize the GPU as much as possible by starting with the entire input.
-        If it doesn't fit on the GPU, half the input is tried.
-        If that doesn't fit halving again, and so on.
-        The largest successful batch size will be remembered for successive calls to the function.
-
-        Args:
-            function: The function to be executed.
-            input_: The tensor input.
-
-        Returns:
-            A numpy array with the result after concatenating the results of each batch.
-
-        """
-        if self.device == "cpu":
-            return function(input_.to(self.device)).numpy()
-
-        n, *_ = input_.shape
-        if function in self.max_batch_size:
-            bs = self.max_batch_size[function]
-        else:
-            bs = input_.shape[0]
-
-        bs_updated = False
-        while bs > 0:
-            try:
-                out = []
-                for i in range(n // bs + 1):
-                    start = i * bs
-                    stop = (i + 1) * bs
-
-                    if start >= n:
-                        break
-
-                    batch_out = function(input_[start:stop].to(self.device)).cpu()
-                    out.append(batch_out)
-
-                if bs_updated:
-                    self.max_batch_size[function] = bs
-
-                if len(out) == 1:
-                    return out[0].numpy()
-
-                return torch.concat(out, dim=0).numpy()
-
-            except torch.cuda.OutOfMemoryError:  # type: ignore
-                torch.cuda.empty_cache()
-                bs = bs // 2
-                bs_updated = True
-
-        raise RuntimeError(
-            'Not enough GPU memory to compute embeddings. Consider disabling GPU with the `CUDA_VISIBLE_DEVICES=""'
-        )
-
-    def _ensure_text_embeddings_enabled(self):
-        if not self._supports_text_embeddings:
-            raise RuntimeError("Embedder does not support text embeddings")
-
-    def embed_text(self, text: str) -> np.ndarray:
-        self._ensure_text_embeddings_enabled()
-        return self.embed_texts([text]).squeeze()
-
-    def embed_image(self, image: Image.Image):
-        return self.embed_images([image]).squeeze()
-
-    def embed_texts(self, texts: list[str]) -> np.ndarray:
-        """
-        If the `supports_text_embeddings` flag is enabled in the `__init__`
-        command, provide a function for embedding text.
-
-        Args:
-            texts: The list of length N of text that should be embedded.
-
-        Returns:
-            A numpy array of shape [N, -1] with the result after concatenating
-            the results of each text.
-
-        """
-        self._ensure_text_embeddings_enabled()
-        return np.empty((0, 512))
-
-    @abstractmethod
-    def embed_images(self, images: list[Image.Image]) -> np.ndarray:
-        """
-        The function for embeddings images. Note that you can utililze the
-        `self.execute_with_largest_batch_size` function to utilize the GPU
-        optimally.
-
-        Args:
-            images: The list (of length N) of images that should be embedded.
-
-        Returns:
-            A numpy array of shape [N, -1] with the resulting embeddings after
-            concatenating the results of each image.
-        """
-        ...
diff --git a/src/encord_active/lib/embeddings/utils.py b/src/encord_active/lib/embeddings/utils.py
index ba0026869..c37087327 100644
--- a/src/encord_active/lib/embeddings/utils.py
+++ b/src/encord_active/lib/embeddings/utils.py
@@ -9,7 +9,7 @@
 from faiss import IndexFlatL2
 from pandera.typing import Series
 
-from encord_active.lib.metrics.types import EmbeddingType
+from encord_active.lib.metrics.metric import EmbeddingType
 from encord_active.lib.metrics.utils import IdentifierSchema
 
 
diff --git a/src/encord_active/lib/encord/project_sync.py b/src/encord_active/lib/encord/project_sync.py
index a32a8f1b6..574284fbb 100644
--- a/src/encord_active/lib/encord/project_sync.py
+++ b/src/encord_active/lib/encord/project_sync.py
@@ -12,12 +12,12 @@
 from encord_active.lib.db.connection import DBConnection
 from encord_active.lib.db.merged_metrics import MergedMetrics
 from encord_active.lib.embeddings.utils import (
+    EMBEDDING_REDUCED_TO_FILENAME,
     LabelEmbedding,
     load_collections,
     save_collections,
 )
-from encord_active.lib.metrics.metric import MetricMetadata
-from encord_active.lib.metrics.types import EmbeddingType
+from encord_active.lib.metrics.metric import EmbeddingType, MetricMetadata
 from encord_active.lib.project import ProjectFileStructure
 from encord_active.lib.project.metadata import fetch_project_meta
 
@@ -51,7 +51,7 @@ def _update_identifiers(identifier: str):
         new_lr, new_du = renaming_map.get(old_lr, old_lr), renaming_map.get(old_du, old_du)
         return identifier.replace(old_du, new_du).replace(old_lr, new_lr)
 
-    embedding_file = project_file_structure.get_embeddings_file(embedding_type, reduced=True)
+    embedding_file = project_file_structure.embeddings / EMBEDDING_REDUCED_TO_FILENAME[embedding_type]
     if not embedding_file.is_file():
         return
 
@@ -133,14 +133,14 @@ def create_filtered_embeddings(
         save_collections(embedding_type, target_project_structure.embeddings, collection)
 
     for embedding_type in [EmbeddingType.IMAGE, EmbeddingType.CLASSIFICATION, EmbeddingType.OBJECT]:
-        curr_embedding_file = curr_project_structure.get_embeddings_file(embedding_type)
-        if not curr_embedding_file.exists():
+        embedding_file_name = EMBEDDING_REDUCED_TO_FILENAME[embedding_type]
+        if not (curr_project_structure.embeddings / embedding_file_name).exists():
             continue
-        embeddings = pickle.loads(curr_embedding_file.read_bytes())
+        embeddings = pickle.loads(Path(curr_project_structure.embeddings / embedding_file_name).read_bytes())
         embeddings_df = pd.DataFrame.from_dict(embeddings)
         embeddings_df = embeddings_df[embeddings_df["identifier"].isin(filtered_df.identifier)]
         filtered_embeddings = embeddings_df.to_dict(orient="list")
-        target_project_structure.get_embeddings_file(embedding_type).write_bytes(pickle.dumps(filtered_embeddings))
+        (target_project_structure.embeddings / embedding_file_name).write_bytes(pickle.dumps(filtered_embeddings))
 
 
 def get_filtered_objects(filtered_labels, label_row_hash, data_unit_hash, objects):
diff --git a/src/encord_active/lib/metrics/acquisition_functions.py b/src/encord_active/lib/metrics/acquisition_functions.py
index 09c926efb..a42747a4a 100644
--- a/src/encord_active/lib/metrics/acquisition_functions.py
+++ b/src/encord_active/lib/metrics/acquisition_functions.py
@@ -8,11 +8,11 @@
 from encord_active.lib.common.iterator import Iterator
 from encord_active.lib.labels.classification import ClassificationType
 from encord_active.lib.labels.object import ObjectShape
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import (
+from encord_active.lib.metrics.metric import (
     AnnotationType,
     DataType,
     EmbeddingType,
+    Metric,
     MetricType,
 )
 from encord_active.lib.metrics.writer import CSVMetricWriter
diff --git a/src/encord_active/lib/metrics/example.py b/src/encord_active/lib/metrics/example.py
index 88858e15c..6f601920e 100644
--- a/src/encord_active/lib/metrics/example.py
+++ b/src/encord_active/lib/metrics/example.py
@@ -1,8 +1,12 @@
 from loguru import logger
 
 from encord_active.lib.common.iterator import Iterator
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
diff --git a/src/encord_active/lib/metrics/execute.py b/src/encord_active/lib/metrics/execute.py
index b42b7d427..70b20c9f3 100644
--- a/src/encord_active/lib/metrics/execute.py
+++ b/src/encord_active/lib/metrics/execute.py
@@ -16,8 +16,15 @@
 from encord_active.lib.common.writer import StatisticsObserver
 from encord_active.lib.labels.classification import ClassificationType
 from encord_active.lib.labels.object import ObjectShape
-from encord_active.lib.metrics.metric import Metric, SimpleMetric, StatsMetadata
-from encord_active.lib.metrics.types import EmbeddingType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    EmbeddingType,
+    Metric,
+    MetricType,
+    SimpleMetric,
+    StatsMetadata,
+)
 from encord_active.lib.metrics.utils import get_embedding_type
 from encord_active.lib.metrics.writer import CSVMetricWriter
 from encord_active.lib.model_predictions.writer import MainPredictionType
@@ -26,9 +33,7 @@
 logger = logger.opt(colors=True)
 
 
-def get_metrics(
-    module: Optional[Union[str, list[str]]] = None, filter_func: Callable[[Type[Metric]], bool] = lambda x: True
-):
+def get_metrics(module: Optional[Union[str, list[str]]] = None, filter_func=lambda x: True):
     if module is None:
         module = ["geometric", "heuristic", "semantic"]
     elif isinstance(module, str):
@@ -78,6 +83,18 @@ def run_metrics_by_embedding_type(embedding_type: EmbeddingType, **kwargs):
     execute_metrics(metrics, **kwargs)
 
 
+def run_all_heuristic_metrics():
+    run_metrics(filter_func=lambda x: x.metadata.metric_type == MetricType.HEURISTIC)
+
+
+def run_all_image_metrics():
+    run_metrics(filter_func=lambda x: x.metadata.data_type == DataType.IMAGE)
+
+
+def run_all_polygon_metrics():
+    run_metrics(filter_func=lambda x: x.metadata.annotation_type in [AnnotationType.OBJECT.POLYGON, AnnotationType.ALL])
+
+
 def run_all_prediction_metrics(**kwargs):
     # Return all metrics that apply according to the prediction type.
     def filter_objects(m: Type[Metric]):
@@ -92,7 +109,7 @@ def filter_objects(m: Type[Metric]):
         else:
             return isinstance(at, ObjectShape)
 
-    def filter_classifications(m: Type[Metric]) -> bool:
+    def filter_classifications(m: Type[Metric]):
         at = m().metadata.annotation_type  # type: ignore
 
         if isinstance(at, list):
@@ -111,7 +128,7 @@ def filter_classifications(m: Type[Metric]) -> bool:
         raise ValueError(f"Undefined prediction type {kwargs['prediction_type']}")
 
 
-def run_metrics(filter_func: Callable[[Type[Metric]], bool] = lambda x: True, **kwargs):
+def run_metrics(filter_func: Callable[[Metric], bool] = lambda x: True, **kwargs):
     metrics = list(map(load_metric, get_metrics(filter_func=filter_func)))
     execute_metrics(metrics, **kwargs)
 
diff --git a/src/encord_active/lib/metrics/geometric/annotation_duplicates.py b/src/encord_active/lib/metrics/geometric/annotation_duplicates.py
index da54d0fa4..fb4c068f3 100644
--- a/src/encord_active/lib/metrics/geometric/annotation_duplicates.py
+++ b/src/encord_active/lib/metrics/geometric/annotation_duplicates.py
@@ -2,8 +2,12 @@
 
 from encord_active.lib.common.iterator import Iterator
 from encord_active.lib.common.utils import get_iou, get_polygon
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
diff --git a/src/encord_active/lib/metrics/geometric/hu_static.py b/src/encord_active/lib/metrics/geometric/hu_static.py
index 19a7b17d0..cc688c5ca 100644
--- a/src/encord_active/lib/metrics/geometric/hu_static.py
+++ b/src/encord_active/lib/metrics/geometric/hu_static.py
@@ -8,8 +8,12 @@
 from encord_active.lib.common.utils import get_object_coordinates, patch_sklearn_linalg
 from encord_active.lib.embeddings.hu_moments import get_hu_embeddings
 from encord_active.lib.embeddings.writer import CSVEmbeddingWriter
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
diff --git a/src/encord_active/lib/metrics/geometric/hu_temporal.py b/src/encord_active/lib/metrics/geometric/hu_temporal.py
index eedfce229..30503d535 100644
--- a/src/encord_active/lib/metrics/geometric/hu_temporal.py
+++ b/src/encord_active/lib/metrics/geometric/hu_temporal.py
@@ -5,8 +5,12 @@
 
 from encord_active.lib.common.iterator import Iterator
 from encord_active.lib.embeddings.hu_moments import get_hu_embeddings
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
diff --git a/src/encord_active/lib/metrics/geometric/image_border_closeness.py b/src/encord_active/lib/metrics/geometric/image_border_closeness.py
index b11aebe17..4eb9b0be5 100644
--- a/src/encord_active/lib/metrics/geometric/image_border_closeness.py
+++ b/src/encord_active/lib/metrics/geometric/image_border_closeness.py
@@ -2,8 +2,12 @@
 
 from encord_active.lib.common.iterator import Iterator
 from encord_active.lib.common.utils import get_object_coordinates
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
diff --git a/src/encord_active/lib/metrics/geometric/object_size.py b/src/encord_active/lib/metrics/geometric/object_size.py
index 51f585c9e..76e55d005 100644
--- a/src/encord_active/lib/metrics/geometric/object_size.py
+++ b/src/encord_active/lib/metrics/geometric/object_size.py
@@ -8,8 +8,12 @@
     get_polygon,
 )
 from encord_active.lib.labels.object import BoxShapes, ObjectShape
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
diff --git a/src/encord_active/lib/metrics/geometric/occlusion_detection_video.py b/src/encord_active/lib/metrics/geometric/occlusion_detection_video.py
index a09403e27..90c43a197 100644
--- a/src/encord_active/lib/metrics/geometric/occlusion_detection_video.py
+++ b/src/encord_active/lib/metrics/geometric/occlusion_detection_video.py
@@ -4,8 +4,12 @@
 from tqdm.auto import tqdm
 
 from encord_active.lib.common.iterator import Iterator
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
diff --git a/src/encord_active/lib/metrics/heuristic/_annotation_time.py b/src/encord_active/lib/metrics/heuristic/_annotation_time.py
index b99ac64dd..d6481704d 100644
--- a/src/encord_active/lib/metrics/heuristic/_annotation_time.py
+++ b/src/encord_active/lib/metrics/heuristic/_annotation_time.py
@@ -1,8 +1,12 @@
 from loguru import logger
 
 from encord_active.lib.common.iterator import Iterator
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
diff --git a/src/encord_active/lib/metrics/heuristic/high_iou_changing_classes.py b/src/encord_active/lib/metrics/heuristic/high_iou_changing_classes.py
index 5d240a03c..4f587b1de 100644
--- a/src/encord_active/lib/metrics/heuristic/high_iou_changing_classes.py
+++ b/src/encord_active/lib/metrics/heuristic/high_iou_changing_classes.py
@@ -2,8 +2,12 @@
 
 from encord_active.lib.common.iterator import Iterator
 from encord_active.lib.common.utils import get_iou, get_polygon
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
diff --git a/src/encord_active/lib/metrics/heuristic/img_features.py b/src/encord_active/lib/metrics/heuristic/img_features.py
index 2e1872c3d..1cd9b4689 100644
--- a/src/encord_active/lib/metrics/heuristic/img_features.py
+++ b/src/encord_active/lib/metrics/heuristic/img_features.py
@@ -5,8 +5,13 @@
 
 from encord_active.lib.common.iterator import Iterator
 from encord_active.lib.common.utils import get_du_size
-from encord_active.lib.metrics.metric import Metric, SimpleMetric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+    SimpleMetric,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 
@@ -39,6 +44,7 @@ def __init__(
             saturation_filters=[50, 255],
             value_filters=[20, 255],
         ):
+
             super().__init__(
                 title=f"{color_name} Values".title(),
                 short_description=f"Ranks images by how {color_name.lower()} the average value of the image is.",
diff --git a/src/encord_active/lib/metrics/heuristic/missing_objects_and_wrong_tracks.py b/src/encord_active/lib/metrics/heuristic/missing_objects_and_wrong_tracks.py
index ccfec91d9..0ae3045eb 100644
--- a/src/encord_active/lib/metrics/heuristic/missing_objects_and_wrong_tracks.py
+++ b/src/encord_active/lib/metrics/heuristic/missing_objects_and_wrong_tracks.py
@@ -9,8 +9,12 @@
 
 from encord_active.lib.common.iterator import Iterator
 from encord_active.lib.common.utils import get_iou, get_polygon
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
diff --git a/src/encord_active/lib/metrics/heuristic/object_counting.py b/src/encord_active/lib/metrics/heuristic/object_counting.py
index f054cf8a7..9d69801de 100644
--- a/src/encord_active/lib/metrics/heuristic/object_counting.py
+++ b/src/encord_active/lib/metrics/heuristic/object_counting.py
@@ -1,6 +1,10 @@
 from encord_active.lib.common.iterator import Iterator
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 
diff --git a/src/encord_active/lib/metrics/heuristic/random.py b/src/encord_active/lib/metrics/heuristic/random.py
index 4c655a877..9aea2d2bc 100644
--- a/src/encord_active/lib/metrics/heuristic/random.py
+++ b/src/encord_active/lib/metrics/heuristic/random.py
@@ -1,8 +1,12 @@
 import numpy as np
 
 from encord_active.lib.common.iterator import Iterator
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 
diff --git a/src/encord_active/lib/metrics/metric.py b/src/encord_active/lib/metrics/metric.py
index 5b7101687..ee7f74fc4 100644
--- a/src/encord_active/lib/metrics/metric.py
+++ b/src/encord_active/lib/metrics/metric.py
@@ -1,24 +1,46 @@
 from abc import ABC, abstractmethod
+from enum import Enum
 from hashlib import md5
-from typing import List, Optional
+from typing import List, Optional, Union
 
 import numpy as np
 from pydantic import BaseModel
 
 from encord_active.lib.common.iterator import Iterator
 from encord_active.lib.common.writer import StatisticsObserver
-
-# TODO: delete AnnotationType import on major version bump 👇
-from encord_active.lib.metrics.types import (  # pylint: disable=unused-import
-    AnnotationType,
-    AnnotationTypeUnion,
-    DataType,
-    EmbeddingType,
-    MetricType,
-)
+from encord_active.lib.labels.classification import ClassificationType
+from encord_active.lib.labels.object import ObjectShape
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 
+class MetricType(str, Enum):
+    SEMANTIC = "semantic"
+    GEOMETRIC = "geometric"
+    HEURISTIC = "heuristic"
+
+
+class DataType(str, Enum):
+    IMAGE = "image"
+    SEQUENCE = "sequence"
+
+
+class EmbeddingType(str, Enum):
+    CLASSIFICATION = "classification"
+    OBJECT = "object"
+    HU_MOMENTS = "hu_moments"
+    IMAGE = "image"
+
+
+AnnotationTypeUnion = Union[ObjectShape, ClassificationType]
+
+
+class AnnotationType:
+    NONE: List[AnnotationTypeUnion] = []
+    OBJECT = ObjectShape
+    CLASSIFICATION = ClassificationType
+    ALL = [*OBJECT, *CLASSIFICATION]
+
+
 class StatsMetadata(BaseModel):
     min_value: float = 0.0
     max_value: float = 0.0
@@ -59,7 +81,7 @@ def __init__(
         long_description: str,
         metric_type: MetricType,
         data_type: DataType,
-        annotation_type: List[AnnotationTypeUnion],
+        annotation_type: List[Union[ObjectShape, ClassificationType]],
         embedding_type: Optional[EmbeddingType] = None,
         doc_url: Optional[str] = None,
     ):
@@ -88,7 +110,7 @@ def __init__(
         long_description: str,
         metric_type: MetricType,
         data_type: DataType,
-        annotation_type: List[AnnotationTypeUnion] = [],
+        annotation_type: List[Union[ObjectShape, ClassificationType]] = [],
         embedding_type: Optional[EmbeddingType] = None,
         doc_url: Optional[str] = None,
     ):
diff --git a/src/encord_active/lib/metrics/semantic/_class_uncertainty.py b/src/encord_active/lib/metrics/semantic/_class_uncertainty.py
index 02cdf4c79..dc201b6af 100644
--- a/src/encord_active/lib/metrics/semantic/_class_uncertainty.py
+++ b/src/encord_active/lib/metrics/semantic/_class_uncertainty.py
@@ -10,12 +10,12 @@
 from torch.nn import LeakyReLU
 
 from encord_active.lib.common.iterator import Iterator
-from encord_active.lib.embeddings.embeddings import get_embeddings
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import (
+from encord_active.lib.embeddings.cnn import get_cnn_embeddings
+from encord_active.lib.metrics.metric import (
     AnnotationType,
     DataType,
     EmbeddingType,
+    Metric,
     MetricType,
 )
 from encord_active.lib.metrics.writer import CSVMetricWriter
@@ -181,7 +181,7 @@ def get_batches_and_model(resnet_embeddings_df):
 def preliminaries(iterator):
     model_path = os.path.join(iterator.cache_dir, "models", f"{Path(__file__).stem}_classifier.pt")
     os.makedirs(os.path.dirname(model_path), exist_ok=True)
-    resnet_embeddings_df = get_embeddings(iterator, embedding_type=EmbeddingType.OBJECT, force=False)
+    resnet_embeddings_df = get_cnn_embeddings(iterator, embedding_type=EmbeddingType.OBJECT, force=False)
     batches, classifier, idx_to_counts, name_to_idx = get_batches_and_model(resnet_embeddings_df)
     if not os.path.isfile(model_path):
         train_model(classifier, model_path, batches, idx_to_counts, name_to_idx)
diff --git a/src/encord_active/lib/metrics/semantic/_heatmap_uncertainty.py b/src/encord_active/lib/metrics/semantic/_heatmap_uncertainty.py
index b796bfc56..4e9c24a99 100644
--- a/src/encord_active/lib/metrics/semantic/_heatmap_uncertainty.py
+++ b/src/encord_active/lib/metrics/semantic/_heatmap_uncertainty.py
@@ -12,9 +12,13 @@
 from torchvision.models.segmentation import DeepLabV3_MobileNet_V3_Large_Weights
 
 from encord_active.lib.common.iterator import Iterator
-from encord_active.lib.metrics.metric import Metric
+from encord_active.lib.metrics.metric import (
+    AnnotationType,
+    DataType,
+    Metric,
+    MetricType,
+)
 from encord_active.lib.metrics.semantic._class_uncertainty import train_test_split
-from encord_active.lib.metrics.types import AnnotationType, DataType, MetricType
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
diff --git a/src/encord_active/lib/metrics/semantic/image_difficulty.py b/src/encord_active/lib/metrics/semantic/image_difficulty.py
index 535aabbda..b0861e241 100644
--- a/src/encord_active/lib/metrics/semantic/image_difficulty.py
+++ b/src/encord_active/lib/metrics/semantic/image_difficulty.py
@@ -6,11 +6,10 @@
 from sklearn.cluster import KMeans
 
 from encord_active.lib.common.iterator import Iterator
-from encord_active.lib.embeddings.embeddings import get_embeddings
+from encord_active.lib.embeddings.cnn import get_cnn_embeddings
 from encord_active.lib.embeddings.utils import LabelEmbedding
 from encord_active.lib.labels.classification import ClassificationType
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import DataType, EmbeddingType, MetricType
+from encord_active.lib.metrics.metric import DataType, EmbeddingType, Metric, MetricType
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
@@ -91,7 +90,7 @@ def _get_difficulty_ranking(self, cluster_size: int) -> Dict[str, int]:
 
     def execute(self, iterator: Iterator, writer: CSVMetricWriter):
         if self.metadata.embedding_type:
-            self.collections = get_embeddings(iterator, embedding_type=self.metadata.embedding_type)
+            self.collections = get_cnn_embeddings(iterator, embedding_type=self.metadata.embedding_type)
         else:
             logger.error(
                 f"<yellow>[Skipping]</yellow> No `embedding_type` provided for the {self.metadata.title} metric!"
@@ -106,9 +105,7 @@ def execute(self, iterator: Iterator, writer: CSVMetricWriter):
 
             data_hash_to_score = self._get_difficulty_ranking(cluster_size)
 
-            for data_unit, _ in iterator.iterate(desc="Writing scores to a file"):
-                score = data_hash_to_score.get(data_unit["data_hash"])
-                if score is not None:
-                    writer.write(score=score)
+            for data_unit, img_pth in iterator.iterate(desc="Writing scores to a file"):
+                writer.write(score=data_hash_to_score[data_unit["data_hash"]])
         else:
             logger.info("<yellow>[Skipping]</yellow> The embedding file is empty.")
diff --git a/src/encord_active/lib/metrics/semantic/image_singularity.py b/src/encord_active/lib/metrics/semantic/image_singularity.py
index 5456a5e9c..81bc4d646 100644
--- a/src/encord_active/lib/metrics/semantic/image_singularity.py
+++ b/src/encord_active/lib/metrics/semantic/image_singularity.py
@@ -10,10 +10,9 @@
 from encord_active.lib.common.utils import (
     fix_duplicate_image_orders_in_knn_graph_all_rows,
 )
-from encord_active.lib.embeddings.embeddings import get_embeddings
+from encord_active.lib.embeddings.cnn import get_cnn_embeddings
 from encord_active.lib.embeddings.utils import LabelEmbedding
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import DataType, EmbeddingType, MetricType
+from encord_active.lib.metrics.metric import DataType, EmbeddingType, Metric, MetricType
 from encord_active.lib.metrics.writer import CSVMetricWriter
 
 logger = logger.opt(colors=True)
@@ -88,7 +87,7 @@ def score_images(self, project_hash: str, nearest_distances: np.ndarray, nearest
 
     def execute(self, iterator: Iterator, writer: CSVMetricWriter):
         if self.metadata.embedding_type:
-            self.collections = get_embeddings(iterator, embedding_type=self.metadata.embedding_type)
+            self.collections = get_cnn_embeddings(iterator, embedding_type=self.metadata.embedding_type)
         else:
             logger.error(
                 f"<yellow>[Skipping]</yellow> No `embedding_type` provided for the {self.metadata.title} metric!"
@@ -96,6 +95,7 @@ def execute(self, iterator: Iterator, writer: CSVMetricWriter):
             return
 
         if len(self.collections) > 0:
+
             embeddings, db_index = self.convert_to_index()
             # For more information why we set the below threshold
             # see here: https://github.com/facebookresearch/faiss/wiki/Implementation-notes#matrix-multiplication-to-do-many-l2-distance-computations
@@ -112,10 +112,10 @@ def execute(self, iterator: Iterator, writer: CSVMetricWriter):
         else:
             logger.info("<yellow>[Skipping]</yellow> The embedding file is empty.")
 
-        for data_unit, _ in iterator.iterate(desc="Writing scores to a file"):
-            data_unit_info = self.scores.get(data_unit["data_hash"])
-            if data_unit_info is not None:
-                writer.write(
-                    score=float(data_unit_info.score),
-                    description=data_unit_info.description,
-                )
+        for data_unit, img_pth in iterator.iterate(desc="Writing scores to a file"):
+
+            data_unit_info = self.scores[data_unit["data_hash"]]
+            writer.write(
+                score=float(data_unit_info.score),
+                description=data_unit_info.description,
+            )
diff --git a/src/encord_active/lib/metrics/semantic/img_classification_quality.py b/src/encord_active/lib/metrics/semantic/img_classification_quality.py
index 47ebe666f..23fa555be 100644
--- a/src/encord_active/lib/metrics/semantic/img_classification_quality.py
+++ b/src/encord_active/lib/metrics/semantic/img_classification_quality.py
@@ -9,16 +9,16 @@
 from loguru import logger
 
 from encord_active.lib.common.iterator import Iterator
+from encord_active.lib.embeddings.cnn import get_cnn_embeddings
 from encord_active.lib.embeddings.dimensionality_reduction import (
     generate_2d_embedding_data,
 )
-from encord_active.lib.embeddings.embeddings import get_embeddings
 from encord_active.lib.embeddings.utils import LabelEmbedding
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import (
+from encord_active.lib.metrics.metric import (
     AnnotationType,
     DataType,
     EmbeddingType,
+    Metric,
     MetricType,
 )
 from encord_active.lib.metrics.utils import is_multiclass_ontology
@@ -181,6 +181,7 @@ def convert_nearest_labels_to_scores_for_all_questions(
         return collections_scores_all_questions
 
     def create_key_score_pairs(self, nearest_indexes: dict[str, np.ndarray]):
+
         nearest_labels_all_questions = self.transform_neighbors_to_labels_for_all_questions(nearest_indexes)
         collections_scores_all_questions = self.convert_nearest_labels_to_scores_for_all_questions(
             nearest_labels_all_questions
@@ -266,11 +267,11 @@ def execute(self, iterator: Iterator, writer: CSVMetricWriter):
             logger.info("<yellow>[Skipping]</yellow> No frame level classifications in the project ontology.")
 
         # TODO: move me somewhere else, this is here to ensure the generation of image embeddings
-        get_embeddings(iterator, embedding_type=EmbeddingType.IMAGE)
+        get_cnn_embeddings(iterator, embedding_type=EmbeddingType.IMAGE)
         generate_2d_embedding_data(EmbeddingType.IMAGE, iterator.cache_dir)
 
         if self.metadata.embedding_type:
-            self.collections = get_embeddings(iterator, embedding_type=self.metadata.embedding_type)
+            self.collections = get_cnn_embeddings(iterator, embedding_type=self.metadata.embedding_type)
         else:
             logger.error(
                 f"<yellow>[Skipping]</yellow> No `embedding_type` provided for the {self.metadata.title} metric!"
diff --git a/src/encord_active/lib/metrics/semantic/img_object_quality.py b/src/encord_active/lib/metrics/semantic/img_object_quality.py
index 1fde9a6ae..717812e20 100644
--- a/src/encord_active/lib/metrics/semantic/img_object_quality.py
+++ b/src/encord_active/lib/metrics/semantic/img_object_quality.py
@@ -10,16 +10,16 @@
 from encord_active.lib.common.utils import (
     fix_duplicate_image_orders_in_knn_graph_all_rows,
 )
+from encord_active.lib.embeddings.cnn import get_cnn_embeddings
 from encord_active.lib.embeddings.dimensionality_reduction import (
     generate_2d_embedding_data,
 )
-from encord_active.lib.embeddings.embeddings import get_embeddings
 from encord_active.lib.embeddings.utils import LabelEmbedding
-from encord_active.lib.metrics.metric import Metric
-from encord_active.lib.metrics.types import (
+from encord_active.lib.metrics.metric import (
     AnnotationType,
     DataType,
     EmbeddingType,
+    Metric,
     MetricType,
 )
 from encord_active.lib.metrics.writer import CSVMetricWriter
@@ -117,7 +117,7 @@ def execute(self, iterator: Iterator, writer: CSVMetricWriter):
             return
 
         if self.metadata.embedding_type:
-            collections = get_embeddings(iterator, embedding_type=self.metadata.embedding_type)
+            collections = get_cnn_embeddings(iterator, embedding_type=self.metadata.embedding_type)
         else:
             logger.error(
                 f"<yellow>[Skipping]</yellow> No `embedding_type` provided for the {self.metadata.title} metric!"
diff --git a/src/encord_active/lib/metrics/types.py b/src/encord_active/lib/metrics/types.py
deleted file mode 100644
index a4ed43211..000000000
--- a/src/encord_active/lib/metrics/types.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from enum import Enum
-from typing import Union
-
-from encord_active.lib.labels.classification import ClassificationType
-from encord_active.lib.labels.object import ObjectShape
-
-AnnotationTypeUnion = Union[ObjectShape, ClassificationType]
-
-
-class AnnotationType:
-    NONE: list[AnnotationTypeUnion] = []
-    OBJECT = ObjectShape
-    CLASSIFICATION = ClassificationType
-    ALL = [*OBJECT, *CLASSIFICATION]
-
-
-class MetricType(str, Enum):
-    SEMANTIC = "semantic"
-    GEOMETRIC = "geometric"
-    HEURISTIC = "heuristic"
-
-
-class DataType(str, Enum):
-    IMAGE = "image"
-    SEQUENCE = "sequence"
-
-
-class EmbeddingType(str, Enum):
-    CLASSIFICATION = "classification"
-    OBJECT = "object"
-    HU_MOMENTS = "hu_moments"
-    IMAGE = "image"
diff --git a/src/encord_active/lib/metrics/utils.py b/src/encord_active/lib/metrics/utils.py
index 6c1481bbc..d0286d248 100644
--- a/src/encord_active/lib/metrics/utils.py
+++ b/src/encord_active/lib/metrics/utils.py
@@ -13,11 +13,12 @@
 from pandera.typing import DataFrame, Series
 from pydantic import ValidationError
 
-from encord_active.lib.metrics.metric import MetricMetadata, StatsMetadata
-from encord_active.lib.metrics.types import (
+from encord_active.lib.metrics.metric import (
     AnnotationType,
     AnnotationTypeUnion,
     EmbeddingType,
+    MetricMetadata,
+    StatsMetadata,
 )
 
 
diff --git a/src/encord_active/lib/project/project_file_structure.py b/src/encord_active/lib/project/project_file_structure.py
index c219da261..8aa575def 100644
--- a/src/encord_active/lib/project/project_file_structure.py
+++ b/src/encord_active/lib/project/project_file_structure.py
@@ -16,19 +16,6 @@
 
 from encord_active.lib.db.connection import PrismaConnection
 from encord_active.lib.file_structure.base import BaseProjectFileStructure
-from encord_active.lib.metrics.types import EmbeddingType
-
-EMBEDDING_TYPE_TO_FILENAME = {
-    EmbeddingType.IMAGE: "cnn_images.pkl",
-    EmbeddingType.CLASSIFICATION: "cnn_classifications.pkl",
-    EmbeddingType.OBJECT: "cnn_objects.pkl",
-}
-
-EMBEDDING_REDUCED_TO_FILENAME = {
-    EmbeddingType.IMAGE: "cnn_images_reduced.pkl",
-    EmbeddingType.CLASSIFICATION: "cnn_classifications_reduced.pkl",
-    EmbeddingType.OBJECT: "cnn_objects_reduced.pkl",
-}
 
 
 # To be deprecated when Encord Active version is >= 0.1.60.
@@ -153,10 +140,6 @@ def metrics_meta(self) -> Path:
     def embeddings(self) -> Path:
         return self.project_dir / "embeddings"
 
-    def get_embeddings_file(self, type_: EmbeddingType, reduced: bool = False) -> Path:
-        lookup = EMBEDDING_REDUCED_TO_FILENAME if reduced else EMBEDDING_TYPE_TO_FILENAME
-        return self.embeddings / lookup[type_]
-
     @property
     def predictions(self) -> Path:
         return self.project_dir / "predictions"
diff --git a/src/encord_active/server/main.py b/src/encord_active/server/main.py
index 6e2df2509..28e220b3c 100644
--- a/src/encord_active/server/main.py
+++ b/src/encord_active/server/main.py
@@ -122,7 +122,7 @@ def get_available_metrics(project: ProjectFileStructureDep, scope: Optional[Metr
 @app.get("/projects/{project}/2d_embeddings/{current_metric}")
 def get_2d_embeddings(project: ProjectFileStructureDep, current_metric: str):
     embedding_type = get_metric_embedding_type(project, current_metric)
-    embeddings_df = get_2d_embedding_data(project, embedding_type)
+    embeddings_df = get_2d_embedding_data(project.embeddings, embedding_type)
 
     if embeddings_df is None:
         raise HTTPException(
diff --git a/src/encord_active/server/utils.py b/src/encord_active/server/utils.py
index f6b1541f8..fd98abaaf 100644
--- a/src/encord_active/server/utils.py
+++ b/src/encord_active/server/utils.py
@@ -10,7 +10,7 @@
 from encord_active.lib.db.helpers.tags import to_grouped_tags
 from encord_active.lib.embeddings.utils import SimilaritiesFinder
 from encord_active.lib.labels.object import ObjectShape
-from encord_active.lib.metrics.types import EmbeddingType
+from encord_active.lib.metrics.metric import EmbeddingType
 from encord_active.lib.metrics.utils import (
     MetricScope,
     get_embedding_type,