diff --git a/CHANGELOG.md b/CHANGELOG.md
index a6c30a28f..09bdc9eb4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -14,11 +14,28 @@ Starting from version 2.6.1, releases are automatically created when changes are
 
 **Note**: If a tag for the current version already exists, the workflow will skip tag and release creation to avoid duplicates.
 
+### [2.8.0]
+
+#### Added
+
+- 28 new backbone configurations: csatv2, dinov3_vitb16, dinov3_vits16, edgenext_s, efficientnet_em, efficientnetv2_b1, fasternet_t0, fasternet_t1, fasternet_t2, ghostnet_v3, hgnet_v2, mobilenet_edgetpu_v2, mobilenetv4_m, mobilenetv4_s, regnety_016, repvit, rexnet, shvit_s1, shvit_s2, shvit_s3, shvit_s4, starnet_s2, starnet_s3, starnet_s4, swiftformer_l1, swiftformer_l3, swiftformer_s, swiftformer_xs
+
+#### Updated
+
+- MLflow-skinny from ^2.3.1 to 3.8.1
+- Timm from 0.9.12 to 1.0.24
+- Onnxconverter-common from ^1.14.0 to 1.16.0
+
+#### Fixed
+
+- Avoid unbound variable errors on mlflow client properties if mlflow is not enabled.
+- Fix wrong field checked in automatic batch size corner case that could cause an infinite loop.
+
 ### [2.7.3]
 
 #### Fixed
 
-Add checks to ensure mlflow client properties can't be called if mlflow is not enabled to avoid unbound variable errors.
+- Add checks to ensure mlflow client properties can't be called if mlflow is not enabled to avoid unbound variable errors.
 
 ### [2.7.2]
 
diff --git a/poetry.lock b/poetry.lock
index 04f13e84e..80370acfb 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -183,6 +183,30 @@ typing-extensions = ">=4"
 [package.extras]
 tz = ["backports.zoneinfo ; python_version < \"3.9\"", "tzdata"]
 
+[[package]]
+name = "annotated-doc"
+version = "0.0.4"
+description = "Document parameters, class attributes, return types, and variables inline, with Annotated."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "annotated_doc-0.0.4-py3-none-any.whl", hash = "sha256:571ac1dc6991c450b25a9c2d84a3705e2ae7a53467b5d111c24fa8baabbed320"},
+    {file = "annotated_doc-0.0.4.tar.gz", hash = "sha256:fbcda96e87e9c92ad167c2e53839e57503ecfda18804ea28102353485033faa4"},
+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
 [[package]]
 name = "anomalib-orobix"
 version = "0.7.0.dev150"
@@ -219,6 +243,26 @@ files = [
     {file = "antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b"},
 ]
 
+[[package]]
+name = "anyio"
+version = "4.12.1"
+description = "High-level concurrency and networking framework on top of asyncio or Trio"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c"},
+    {file = "anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
+
+[package.extras]
+trio = ["trio (>=0.31.0) ; python_version < \"3.10\"", "trio (>=0.32.0) ; python_version >= \"3.10\""]
+
 [[package]]
 name = "appdirs"
 version = "1.4.4"
@@ -1210,7 +1254,7 @@ version = "1.2.2"
 description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
-groups = ["dev", "test"]
+groups = ["main", "dev", "test"]
 files = [
     {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
     {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
@@ -1234,6 +1278,29 @@ files = [
 [package.extras]
 tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich ; python_version >= \"3.11\""]
 
+[[package]]
+name = "fastapi"
+version = "0.128.1"
+description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "fastapi-0.128.1-py3-none-any.whl", hash = "sha256:ee82146bbf91ea5bbf2bb8629e4c6e056c4fbd997ea6068501b11b15260b50fb"},
+    {file = "fastapi-0.128.1.tar.gz", hash = "sha256:ce5be4fa26d4ce6f54debcc873d1fb8e0e248f5c48d7502ba6c61457ab2dc766"},
+]
+
+[package.dependencies]
+annotated-doc = ">=0.0.2"
+pydantic = ">=2.7.0"
+starlette = ">=0.40.0,<0.51.0"
+typing-extensions = ">=4.8.0"
+
+[package.extras]
+all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (>=0.23.0,<1.0.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=3.1.5)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
+standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (>=0.23.0,<1.0.0)", "jinja2 (>=3.1.5)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
+standard-no-fastapi-cloud-cli = ["email-validator (>=2.0.0)", "fastapi-cli[standard-no-fastapi-cloud-cli] (>=0.0.8)", "httpx (>=0.23.0,<1.0.0)", "jinja2 (>=3.1.5)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
+
 [[package]]
 name = "filelock"
 version = "3.17.0"
@@ -1811,6 +1878,18 @@ files = [
 [package.extras]
 protobuf = ["grpcio-tools (>=1.70.0)"]
 
+[[package]]
+name = "h11"
+version = "0.16.0"
+description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
+    {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
+]
+
 [[package]]
 name = "h5py"
 version = "3.8.0"
@@ -3407,44 +3486,49 @@ typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
 
 [[package]]
 name = "mlflow-skinny"
-version = "2.20.0"
+version = "3.8.1"
 description = "MLflow is an open source platform for the complete machine learning lifecycle"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 groups = ["main"]
 files = [
-    {file = "mlflow_skinny-2.20.0-py3-none-any.whl", hash = "sha256:983559c1fde2ad5debbcaf36f86575d11f28d042673882205195238d12e872f7"},
-    {file = "mlflow_skinny-2.20.0.tar.gz", hash = "sha256:7c3cb1bcb2d258605d2a54d6701d16b72b573bb545b071542ae8b3739ae02904"},
+    {file = "mlflow_skinny-3.8.1-py3-none-any.whl", hash = "sha256:3a6ee27f5ac1e67c1d565fa0e12c070b27129b03e669dcaf88ff841176429142"},
+    {file = "mlflow_skinny-3.8.1.tar.gz", hash = "sha256:0c0aade08187030a4653e267bcd63de2f12cbfebf4c6737832cba45d6fb3594d"},
 ]
 
 [package.dependencies]
-cachetools = ">=5.0.0,<6"
+cachetools = ">=5.0.0,<7"
 click = ">=7.0,<9"
 cloudpickle = "<4"
 databricks-sdk = ">=0.20.0,<1"
+fastapi = "<1"
 gitpython = ">=3.1.9,<4"
 importlib_metadata = ">=3.7.0,<4.7.0 || >4.7.0,<9"
 opentelemetry-api = ">=1.9.0,<3"
+opentelemetry-proto = ">=1.9.0,<3"
 opentelemetry-sdk = ">=1.9.0,<3"
-packaging = "<25"
-protobuf = ">=3.12.0,<6"
-pydantic = ">=1.0,<3"
+packaging = "<26"
+protobuf = ">=3.12.0,<7"
+pydantic = ">=2.0.0,<3"
+python-dotenv = ">=0.19.0,<2"
 pyyaml = ">=5.1,<7"
 requests = ">=2.17.3,<3"
 sqlparse = ">=0.4.0,<1"
 typing-extensions = ">=4.0.0,<5"
+uvicorn = "<1"
 
 [package.extras]
 aliyun-oss = ["aliyunstoreplugin"]
-databricks = ["azure-storage-file-datalake (>12)", "boto3 (>1)", "botocore", "google-cloud-storage (>=1.30.0)"]
+auth = ["Flask-WTF (<2)"]
+databricks = ["azure-storage-file-datalake (>12)", "boto3 (>1)", "botocore", "databricks-agents (>=1.2.0,<2.0)", "google-cloud-storage (>=1.30.0)"]
 extras = ["azureml-core (>=1.2.0)", "boto3", "botocore", "google-cloud-storage (>=1.30.0)", "kubernetes", "prometheus-flask-exporter", "pyarrow", "pysftp", "requests-auth-aws-sigv4", "virtualenv"]
 gateway = ["aiohttp (<4)", "boto3 (>=1.28.56,<2)", "fastapi (<1)", "slowapi (>=0.1.9,<1)", "tiktoken (<1)", "uvicorn[standard] (<1)", "watchfiles (<2)"]
-genai = ["aiohttp (<4)", "boto3 (>=1.28.56,<2)", "fastapi (<1)", "slowapi (>=0.1.9,<1)", "tiktoken (<1)", "uvicorn[standard] (<1)", "watchfiles (<2)"]
+genai = ["aiohttp (<4)", "boto3 (>=1.28.56,<2)", "fastapi (<1)", "litellm (>=1.0.0,<2)", "slowapi (>=0.1.9,<1)", "tiktoken (<1)", "uvicorn[standard] (<1)", "watchfiles (<2)"]
 jfrog = ["mlflow-jfrog-plugin"]
-langchain = ["langchain (>=0.1.0,<=0.3.14)"]
-mlserver = ["mlserver (>=1.2.0,!=1.3.1)", "mlserver-mlflow (>=1.2.0,!=1.3.1)"]
+langchain = ["langchain (>=0.3.12,<=1.1.3)"]
+mcp = ["click (!=8.3.0)", "fastmcp (>=2.0.0,<3)"]
+mlserver = ["mlserver (>=1.2.0,!=1.3.1,<2.0.0)", "mlserver-mlflow (>=1.2.0,!=1.3.1,<2.0.0)"]
 sqlserver = ["mlflow-dbstore"]
-xethub = ["mlflow-xethub"]
 
 [[package]]
 name = "more-itertools"
@@ -4110,22 +4194,26 @@ reference = ["Pillow", "google-re2"]
 
 [[package]]
 name = "onnxconverter-common"
-version = "1.14.0"
+version = "1.16.0"
 description = "ONNX Converter and Optimization Tools"
 optional = true
 python-versions = ">=3.8"
 groups = ["main"]
 markers = "extra == \"onnx\""
 files = [
-    {file = "onnxconverter-common-1.14.0.tar.gz", hash = "sha256:6e431429bd15325c5b2c3eab61bed0d5634c23ed58f8823961be448d629d014a"},
-    {file = "onnxconverter_common-1.14.0-py2.py3-none-any.whl", hash = "sha256:9723e4a9b47f283e298605dce9f357d5ebd5e5e70172fca26e282a1b490916c4"},
+    {file = "onnxconverter_common-1.16.0-py2.py3-none-any.whl", hash = "sha256:df39ee96f17fff119dff10dd245467651b60b9e8a96020eb93402239794852f7"},
 ]
 
 [package.dependencies]
 numpy = "*"
 onnx = "*"
 packaging = "*"
-protobuf = "3.20.2"
+protobuf = ">=3.20.2"
+
+[package.extras]
+dev = ["onnxconverter-common[lint,test]"]
+lint = ["pyright", "ruff"]
+test = ["pytest", "pytest-cov"]
 
 [[package]]
 name = "onnxruntime-gpu"
@@ -4228,6 +4316,21 @@ files = [
 deprecated = ">=1.2.6"
 setuptools = ">=16.0"
 
+[[package]]
+name = "opentelemetry-proto"
+version = "1.39.1"
+description = "OpenTelemetry Python Proto"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "opentelemetry_proto-1.39.1-py3-none-any.whl", hash = "sha256:22cdc78efd3b3765d09e68bfbd010d4fc254c9818afd0b6b423387d9dee46007"},
+    {file = "opentelemetry_proto-1.39.1.tar.gz", hash = "sha256:6c8e05144fc0d3ed4d22c2289c6b126e03bcd0e6a7da0f16cedd2e1c2772e2c8"},
+]
+
+[package.dependencies]
+protobuf = ">=5.0,<7.0"
+
 [[package]]
 name = "opentelemetry-sdk"
 version = "1.16.0"
@@ -4730,34 +4833,22 @@ files = [
 
 [[package]]
 name = "protobuf"
-version = "3.20.2"
-description = "Protocol Buffers"
+version = "6.33.5"
+description = ""
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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+    {file = "protobuf-6.33.5-cp39-cp39-win32.whl", hash = "sha256:a3157e62729aafb8df6da2c03aa5c0937c7266c626ce11a278b6eb7963c4e37c"},
+    {file = "protobuf-6.33.5-cp39-cp39-win_amd64.whl", hash = "sha256:8f04fa32763dcdb4973d537d6b54e615cc61108c7cb38fe59310c3192d29510a"},
+    {file = "protobuf-6.33.5-py3-none-any.whl", hash = "sha256:69915a973dd0f60f31a08b8318b73eab2bd6a392c79184b3612226b0a3f8ec02"},
+    {file = "protobuf-6.33.5.tar.gz", hash = "sha256:6ddcac2a081f8b7b9642c09406bc6a4290128fce5f471cddd165960bb9119e5c"},
 ]
 
 [[package]]
@@ -4873,56 +4964,137 @@ markers = {dev = "sys_platform == \"linux\" and platform_python_implementation !
 
 [[package]]
 name = "pydantic"
-version = "1.10.10"
-description = "Data validation and settings management using python type hints"
+version = "2.11.10"
+description = "Data validation using Python type hints"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
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+    {file = "pydantic_core-2.33.2.tar.gz", hash = "sha256:7cb8bc3605c29176e1b105350d2e6474142d7c1bd1d9327c4a9bdb46bf827acc"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
 
 [[package]]
 name = "pygments"
@@ -6135,6 +6307,25 @@ pure-eval = "*"
 [package.extras]
 tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
+[[package]]
+name = "starlette"
+version = "0.50.0"
+description = "The little ASGI library that shines."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "starlette-0.50.0-py3-none-any.whl", hash = "sha256:9e5391843ec9b6e472eed1365a78c8098cfceb7a74bfd4d6b1c0c0095efb3bca"},
+    {file = "starlette-0.50.0.tar.gz", hash = "sha256:a2a17b22203254bcbc2e1f926d2d55f3f9497f769416b3190768befe598fa3ca"},
+]
+
+[package.dependencies]
+anyio = ">=3.6.2,<5"
+typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""}
+
+[package.extras]
+full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
+
 [[package]]
 name = "stevedore"
 version = "5.4.0"
@@ -6271,21 +6462,21 @@ zarr = ["fsspec", "zarr (<3)"]
 
 [[package]]
 name = "timm"
-version = "0.9.12"
+version = "1.0.24"
 description = "PyTorch Image Models"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "timm-0.9.12-py3-none-any.whl", hash = "sha256:2a828afac5b710a80ec66d0f85807e171e342faf5c0703b33102d8aa206f19dc"},
-    {file = "timm-0.9.12.tar.gz", hash = "sha256:9121d1cf320f7f32490d893340fd33117bda0a0270eb8282dfd52ae5fd3e1af6"},
+    {file = "timm-1.0.24-py3-none-any.whl", hash = "sha256:8301ac783410c6ad72c73c49326af6d71a9e4d1558238552796e825c2464913f"},
+    {file = "timm-1.0.24.tar.gz", hash = "sha256:c7b909f43fe2ef8fe62c505e270cd4f1af230dfbc37f2ee93e3608492b9d9a40"},
 ]
 
 [package.dependencies]
-huggingface-hub = "*"
+huggingface_hub = "*"
 pyyaml = "*"
 safetensors = "*"
-torch = ">=1.7"
+torch = "*"
 torchvision = "*"
 
 [[package]]
@@ -6685,6 +6876,21 @@ files = [
     {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
 ]
 
+[[package]]
+name = "typing-inspection"
+version = "0.4.2"
+description = "Runtime typing introspection tools"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "typing_inspection-0.4.2-py3-none-any.whl", hash = "sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7"},
+    {file = "typing_inspection-0.4.2.tar.gz", hash = "sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.12.0"
+
 [[package]]
 name = "ujson"
 version = "5.10.0"
@@ -6790,6 +6996,26 @@ brotli = ["brotli (==1.0.9) ; os_name != \"nt\" and python_version < \"3\" and p
 secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress ; python_version == \"2.7\"", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
 socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
+[[package]]
+name = "uvicorn"
+version = "0.40.0"
+description = "The lightning-fast ASGI server."
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "uvicorn-0.40.0-py3-none-any.whl", hash = "sha256:c6c8f55bc8bf13eb6fa9ff87ad62308bbbc33d0b67f84293151efe87e0d5f2ee"},
+    {file = "uvicorn-0.40.0.tar.gz", hash = "sha256:839676675e87e73694518b5574fd0f24c9d97b46bea16df7b8c05ea1a51071ea"},
+]
+
+[package.dependencies]
+click = ">=7.0"
+h11 = ">=0.8"
+typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
+
+[package.extras]
+standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"]
+
 [[package]]
 name = "verspec"
 version = "0.1.0"
@@ -7258,4 +7484,4 @@ onnx = ["onnx", "onnxconverter-common", "onnxruntime_gpu", "onnxsim"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.11"
-content-hash = "81c32fc98ec1614ef30771f6351d0b1112af7dfea4347524ac2c251530b38e85"
+content-hash = "c03f5c770d5bbb907356601d1d14154c9260acfbe24a0996acf4c018c79c8467"
diff --git a/pyproject.toml b/pyproject.toml
index 38cd22d96..76453dc95 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "quadra"
-version = "2.7.3"
+version = "2.8.0"
 description = "Deep Learning experiment orchestration library"
 authors = [
 	"Federico Belotti <federico.belotti@orobix.com>",
@@ -50,7 +50,7 @@ torchmetrics = "~0.10"
 hydra_core = "~1.3"
 hydra_colorlog = "~1.2"
 hydra_optuna_sweeper = "~1.2"
-mlflow-skinny = "^2.3.1"
+mlflow-skinny = "3.8.1"
 boto3 = "~1.26"
 minio = "~7.1"
 tensorboard = "~2.20"
@@ -70,7 +70,7 @@ label_studio_converter = "~0.0"
 scikit_multilearn = "~0.2"
 tripy = "~1.0"
 h5py = "~3.8"
-timm = "0.9.12"
+timm = "1.0.24"
 segmentation_models_pytorch = "0.5.0"
 
 anomalib-orobix = "0.7.0.dev150"
@@ -82,7 +82,7 @@ typing_extensions = { version = "4.11.0", python = "<3.10" }
 onnx = { version = "1.15.0", optional = true }
 onnxsim = { version = "0.4.28", optional = true }
 onnxruntime_gpu = { version = "1.23.2", optional = true }
-onnxconverter-common = { version = "^1.14.0", optional = true }
+onnxconverter-common = { version = "1.16.0", optional = true }
 
 [[tool.poetry.source]]
 name = "torch_cu128"
diff --git a/quadra/__init__.py b/quadra/__init__.py
index 2aa72be5b..0c308dfa4 100644
--- a/quadra/__init__.py
+++ b/quadra/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "2.7.3"
+__version__ = "2.8.0"
 
 
 def get_version():
diff --git a/quadra/configs/backbone/csatv2.yaml b/quadra/configs/backbone/csatv2.yaml
new file mode 100644
index 000000000..5a74bcec8
--- /dev/null
+++ b/quadra/configs/backbone/csatv2.yaml
@@ -0,0 +1,9 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: csatv2.r512_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 512
+  output_dim: 386
+  nb_heads: 8
diff --git a/quadra/configs/backbone/dinov3_vitb16.yaml b/quadra/configs/backbone/dinov3_vitb16.yaml
new file mode 100644
index 000000000..ff8914676
--- /dev/null
+++ b/quadra/configs/backbone/dinov3_vitb16.yaml
@@ -0,0 +1,10 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: vit_base_patch16_dinov3
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 768
+  patch_size: 16
+  nb_heads: 12
diff --git a/quadra/configs/backbone/dinov3_vits16.yaml b/quadra/configs/backbone/dinov3_vits16.yaml
new file mode 100644
index 000000000..69f5e6178
--- /dev/null
+++ b/quadra/configs/backbone/dinov3_vits16.yaml
@@ -0,0 +1,10 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: vit_small_patch16_dinov3
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 384
+  patch_size: 16
+  nb_heads: 6
diff --git a/quadra/configs/backbone/edgenext_s.yaml b/quadra/configs/backbone/edgenext_s.yaml
new file mode 100644
index 000000000..475918a53
--- /dev/null
+++ b/quadra/configs/backbone/edgenext_s.yaml
@@ -0,0 +1,9 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: edgenext_small.usi_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 256
+  output_dim: 304
+  nb_heads: 8
diff --git a/quadra/configs/backbone/efficientnet_em.yaml b/quadra/configs/backbone/efficientnet_em.yaml
new file mode 100644
index 000000000..7313ce7e5
--- /dev/null
+++ b/quadra/configs/backbone/efficientnet_em.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: efficientnet_em.ra2_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 240
+  output_dim: 1280
diff --git a/quadra/configs/backbone/efficientnetv2_b1.yaml b/quadra/configs/backbone/efficientnetv2_b1.yaml
new file mode 100644
index 000000000..c6af00110
--- /dev/null
+++ b/quadra/configs/backbone/efficientnetv2_b1.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: tf_efficientnetv2_b1.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 191
+  output_dim: 1280
diff --git a/quadra/configs/backbone/efficientnetv2_s.yaml b/quadra/configs/backbone/efficientnetv2_s.yaml
index a75311d68..30fb896e3 100644
--- a/quadra/configs/backbone/efficientnetv2_s.yaml
+++ b/quadra/configs/backbone/efficientnetv2_s.yaml
@@ -1,6 +1,6 @@
 model:
   _target_: quadra.models.classification.TimmNetworkBuilder
-  model_name: tf_efficientnetv2_s_in21ft1k
+  model_name: tf_efficientnetv2_s.in21k_ft_in1k
   pretrained: true
   freeze: false
 metadata:
diff --git a/quadra/configs/backbone/fasternet_t0.yaml b/quadra/configs/backbone/fasternet_t0.yaml
new file mode 100644
index 000000000..e79970862
--- /dev/null
+++ b/quadra/configs/backbone/fasternet_t0.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: fasternet_t0.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 1280
diff --git a/quadra/configs/backbone/fasternet_t1.yaml b/quadra/configs/backbone/fasternet_t1.yaml
new file mode 100644
index 000000000..0d79d4228
--- /dev/null
+++ b/quadra/configs/backbone/fasternet_t1.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: fasternet_t1.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 1280
diff --git a/quadra/configs/backbone/fasternet_t2.yaml b/quadra/configs/backbone/fasternet_t2.yaml
new file mode 100644
index 000000000..dd06c4d98
--- /dev/null
+++ b/quadra/configs/backbone/fasternet_t2.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: fasternet_t2.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 1280
diff --git a/quadra/configs/backbone/ghostnet_v3.yaml b/quadra/configs/backbone/ghostnet_v3.yaml
new file mode 100644
index 000000000..f847e3e66
--- /dev/null
+++ b/quadra/configs/backbone/ghostnet_v3.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: ghostnetv3_100.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 1280
diff --git a/quadra/configs/backbone/hgnet_v2.yaml b/quadra/configs/backbone/hgnet_v2.yaml
new file mode 100644
index 000000000..3555166e0
--- /dev/null
+++ b/quadra/configs/backbone/hgnet_v2.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: hgnetv2_b2.ssld_stage2_ft_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 2048
diff --git a/quadra/configs/backbone/mobilenet_edgetpu_v2.yaml b/quadra/configs/backbone/mobilenet_edgetpu_v2.yaml
new file mode 100644
index 000000000..d1f2fb284
--- /dev/null
+++ b/quadra/configs/backbone/mobilenet_edgetpu_v2.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: mobilenet_edgetpu_v2_m.ra4_e3600_r224_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 1344
diff --git a/quadra/configs/backbone/mobilenetv4_m.yaml b/quadra/configs/backbone/mobilenetv4_m.yaml
new file mode 100644
index 000000000..6d9eca761
--- /dev/null
+++ b/quadra/configs/backbone/mobilenetv4_m.yaml
@@ -0,0 +1,9 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: mobilenetv4_hybrid_medium.ix_e550_r384_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 384
+  output_dim: 1280
+  nb_heads: 4
diff --git a/quadra/configs/backbone/mobilenetv4_s.yaml b/quadra/configs/backbone/mobilenetv4_s.yaml
new file mode 100644
index 000000000..9a9ba79c2
--- /dev/null
+++ b/quadra/configs/backbone/mobilenetv4_s.yaml
@@ -0,0 +1,10 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: mobilenetv4_conv_small.e2400_r224_in1k
+  # model_name: mobilenetv4_conv_small.e1200_r224_in1k
+  # model_name: mobilenetv4_conv_small_050.e3000_r224_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 1280
diff --git a/quadra/configs/backbone/regnety_016.yaml b/quadra/configs/backbone/regnety_016.yaml
new file mode 100644
index 000000000..abeb727bf
--- /dev/null
+++ b/quadra/configs/backbone/regnety_016.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: regnety_016.tv2_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 888
diff --git a/quadra/configs/backbone/repvit.yaml b/quadra/configs/backbone/repvit.yaml
new file mode 100644
index 000000000..2fbd13515
--- /dev/null
+++ b/quadra/configs/backbone/repvit.yaml
@@ -0,0 +1,10 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: repvit_m1_1.dist_450e_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 512
+  patch_size: 4
+  nb_heads: 8
diff --git a/quadra/configs/backbone/resnet18_ssl.yaml b/quadra/configs/backbone/resnet18_ssl.yaml
index caf0b2fc7..841b13984 100644
--- a/quadra/configs/backbone/resnet18_ssl.yaml
+++ b/quadra/configs/backbone/resnet18_ssl.yaml
@@ -1,6 +1,6 @@
 model:
   _target_: quadra.models.classification.TimmNetworkBuilder
-  model_name: ssl_resnet18
+  model_name: resnet18.fb_ssl_yfcc100m_ft_in1k
   pretrained: true
   freeze: false
 metadata:
diff --git a/quadra/configs/backbone/rexnet.yaml b/quadra/configs/backbone/rexnet.yaml
new file mode 100644
index 000000000..a13abf467
--- /dev/null
+++ b/quadra/configs/backbone/rexnet.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: rexnet_150.nav_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 1920
diff --git a/quadra/configs/backbone/shvit_s1.yaml b/quadra/configs/backbone/shvit_s1.yaml
new file mode 100644
index 000000000..2b4612222
--- /dev/null
+++ b/quadra/configs/backbone/shvit_s1.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: shvit_s1.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 320
diff --git a/quadra/configs/backbone/shvit_s2.yaml b/quadra/configs/backbone/shvit_s2.yaml
new file mode 100644
index 000000000..9db351e5c
--- /dev/null
+++ b/quadra/configs/backbone/shvit_s2.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: shvit_s2.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 448
diff --git a/quadra/configs/backbone/shvit_s3.yaml b/quadra/configs/backbone/shvit_s3.yaml
new file mode 100644
index 000000000..361fe3359
--- /dev/null
+++ b/quadra/configs/backbone/shvit_s3.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: shvit_s3.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 448
diff --git a/quadra/configs/backbone/shvit_s4.yaml b/quadra/configs/backbone/shvit_s4.yaml
new file mode 100644
index 000000000..cee84264c
--- /dev/null
+++ b/quadra/configs/backbone/shvit_s4.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: shvit_s4.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 256
+  output_dim: 448
diff --git a/quadra/configs/backbone/starnet_s2.yaml b/quadra/configs/backbone/starnet_s2.yaml
new file mode 100644
index 000000000..99b911412
--- /dev/null
+++ b/quadra/configs/backbone/starnet_s2.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: starnet_s2.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 256
diff --git a/quadra/configs/backbone/starnet_s3.yaml b/quadra/configs/backbone/starnet_s3.yaml
new file mode 100644
index 000000000..84375df21
--- /dev/null
+++ b/quadra/configs/backbone/starnet_s3.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: starnet_s3.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 256
diff --git a/quadra/configs/backbone/starnet_s4.yaml b/quadra/configs/backbone/starnet_s4.yaml
new file mode 100644
index 000000000..c4ad09f58
--- /dev/null
+++ b/quadra/configs/backbone/starnet_s4.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: starnet_s4.in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 256
diff --git a/quadra/configs/backbone/swiftformer_l1.yaml b/quadra/configs/backbone/swiftformer_l1.yaml
new file mode 100644
index 000000000..c6ebb1490
--- /dev/null
+++ b/quadra/configs/backbone/swiftformer_l1.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: swiftformer_l1.dist_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 384
diff --git a/quadra/configs/backbone/swiftformer_l3.yaml b/quadra/configs/backbone/swiftformer_l3.yaml
new file mode 100644
index 000000000..6b0f9bd49
--- /dev/null
+++ b/quadra/configs/backbone/swiftformer_l3.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: swiftformer_l3.dist_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 512
diff --git a/quadra/configs/backbone/swiftformer_s.yaml b/quadra/configs/backbone/swiftformer_s.yaml
new file mode 100644
index 000000000..33ea5ff19
--- /dev/null
+++ b/quadra/configs/backbone/swiftformer_s.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: swiftformer_s.dist_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 224
diff --git a/quadra/configs/backbone/swiftformer_xs.yaml b/quadra/configs/backbone/swiftformer_xs.yaml
new file mode 100644
index 000000000..799e8e8cb
--- /dev/null
+++ b/quadra/configs/backbone/swiftformer_xs.yaml
@@ -0,0 +1,8 @@
+model:
+  _target_: quadra.models.classification.TimmNetworkBuilder
+  model_name: swiftformer_xs.dist_in1k
+  pretrained: true
+  freeze: false
+metadata:
+  input_size: 224
+  output_dim: 220
diff --git a/quadra/models/classification/backbones.py b/quadra/models/classification/backbones.py
index 3bf48d954..27ed00d7a 100644
--- a/quadra/models/classification/backbones.py
+++ b/quadra/models/classification/backbones.py
@@ -4,7 +4,7 @@
 
 import timm
 import torch
-from timm.models.helpers import load_checkpoint
+from timm.models import load_checkpoint
 from torch import nn
 from torchvision import models
 
diff --git a/quadra/utils/evaluation.py b/quadra/utils/evaluation.py
index 9857d43e2..156ffc634 100644
--- a/quadra/utils/evaluation.py
+++ b/quadra/utils/evaluation.py
@@ -467,10 +467,10 @@ def wrapper(self, *args, **kwargs):
                     log.warning(
                         "The function %s went out of memory, trying to reduce the batch size to %d",
                         func.__name__,
-                        self.datamodule.batch_size,
+                        getattr(self.datamodule, batch_size_attribute_name),
                     )
 
-                    if self.datamodule.batch_size == 0:
+                    if getattr(self.datamodule, batch_size_attribute_name) == 0:
                         raise RuntimeError(
                             f"Unable to run {func.__name__} with batch size 1, the program will exit"
                         ) from e
diff --git a/quadra/utils/mlflow.py b/quadra/utils/mlflow.py
index 9ebbc4815..2cce4ecee 100644
--- a/quadra/utils/mlflow.py
+++ b/quadra/utils/mlflow.py
@@ -121,6 +121,7 @@ def _log_single_model(
     Returns:
         True if model was uploaded successfully, False otherwise.
     """
+    model_name = os.path.basename(model_path).replace(".", "_")
     if model_type == "pytorch" and not mlflow_zip_models:
         log.warning("Pytorch format still not supported for mlflow upload")
         return False
@@ -128,11 +129,13 @@ def _log_single_model(
     if mlflow_zip_models:
         with TemporaryDirectory() as temp_dir:
             _create_model_zip_archive(model_path, model_type, export_folder, temp_dir)
+            # Set step=-1 to avoid psycopg2.errors.UniqueViolation raised as a result of Query-invoked autoflush
             mlflow.pyfunc.log_model(
-                artifact_path=model_path,
+                name=model_name,
                 loader_module="not.used",
                 data_path=os.path.join(temp_dir, "assets.zip"),
                 pip_requirements=[""],
+                step=-1,
             )
         return True
 
@@ -145,11 +148,8 @@ def _log_single_model(
 
     if model_type in ["torchscript", "pytorch"]:
         signature = infer_signature_model(loaded_model.model, inputs)
-        mlflow.pytorch.log_model(
-            loaded_model.model,
-            artifact_path=model_path,
-            signature=signature,
-        )
+        # Set step=-1 to avoid psycopg2.errors.UniqueViolation raised as a result of Query-invoked autoflush
+        mlflow.pytorch.log_model(loaded_model.model, name=model_name, signature=signature, step=-1)
         return True
 
     if model_type in ["onnx", "simplified_onnx"] and ONNX_AVAILABLE:
@@ -159,11 +159,8 @@ def _log_single_model(
 
         signature = infer_signature_model(loaded_model, inputs)
         model_proto = onnx.load(loaded_model.model_path)
-        mlflow.onnx.log_model(
-            model_proto,
-            artifact_path=model_path,
-            signature=signature,
-        )
+        # Set step=-1 to avoid psycopg2.errors.UniqueViolation raised as a result of Query-invoked autoflush
+        mlflow.onnx.log_model(model_proto, name=model_name, signature=signature, step=-1)
         return True
 
     return False
@@ -396,6 +393,7 @@ def __init__(self, config: DictConfig):
         self._experiment_id: str | None
         self._experiment_name: str = "default"
         self._tracking_uri: str
+        self._run_name: str | None
         self._enabled: bool = False
         self._setup()
 
@@ -426,6 +424,12 @@ def tracking_uri(self) -> str:
 
     def _setup(self) -> None:
         """Determine whether MLflow integration should be active."""
+        # Initialize all properties first
+        self._run_id = None
+        self._experiment_id = None
+        self._tracking_uri = ""
+        self._run_name = None
+
         logger_config = self._config.get("logger")
         if logger_config is None:
             log.info("No logger config found, sklearn MLflow integration disabled")
@@ -444,8 +448,6 @@ def _setup(self) -> None:
         self._experiment_name = mlflow_config.get("experiment_name", self._config.core.get("name", "default"))
         self._run_name = mlflow_config.get("run_name", None)
         self._tracking_uri = tracking_uri
-        self._experiment_id = None
-        self._run_id = None
         self._enabled = True
 
     def start_run(self) -> None:
@@ -551,8 +553,10 @@ def log_sklearn_model(self, model: Any, artifact_path: str) -> None:
             return
 
         try:
-            mlflow.sklearn.log_model(model, artifact_path=artifact_path)
-            log.info("Sklearn model logged to MLflow at artifact_path=%s", artifact_path)
+            model_name = os.path.basename(artifact_path).replace(".", "_")
+            # Set step=-1 to avoid psycopg2.errors.UniqueViolation raised as a result of Query-invoked autoflush
+            mlflow.sklearn.log_model(model, name=model_name, step=-1)
+            log.info("Sklearn model logged to MLflow with name=%s", model_name)
         except Exception as e:
             log.warning("Failed to log sklearn model to MLflow: %s", e)
 
diff --git a/quadra/utils/models.py b/quadra/utils/models.py
index acfc07f3b..fdd3e7fb0 100644
--- a/quadra/utils/models.py
+++ b/quadra/utils/models.py
@@ -13,7 +13,7 @@
 from pytorch_grad_cam import GradCAM
 from scipy import ndimage
 from sklearn.linear_model._base import ClassifierMixin
-from timm.models.layers import DropPath
+from timm.layers import DropPath
 from timm.models.vision_transformer import Mlp
 from torch import nn
 
@@ -127,16 +127,16 @@ def get_feature(
         if not hasattr(feature_extractor, "features_extractor"):
             gradcam = False
         elif isinstance(feature_extractor.features_extractor, timm.models.resnet.ResNet):
-            target_layers = [feature_extractor.features_extractor.layer4[-1]]
+            target_layers = [feature_extractor.features_extractor.layer4[-1]]  # type: ignore[index]
             cam = GradCAM(
                 model=feature_extractor,
                 target_layers=target_layers,
             )
-            for p in feature_extractor.features_extractor.layer4[-1].parameters():
+            for p in feature_extractor.features_extractor.layer4[-1].parameters():  # type: ignore[index,union-attr]
                 p.requires_grad = True
-        elif is_vision_transformer(feature_extractor.features_extractor):
+        elif is_vision_transformer(cast(torch.nn.Module, feature_extractor.features_extractor)):
             grad_rollout = VitAttentionGradRollout(
-                feature_extractor.features_extractor,
+                cast(torch.nn.Module, feature_extractor.features_extractor),
                 classifier=classifier,
                 example_input=None if input_shape is None else torch.randn(1, *input_shape),
             )
@@ -162,7 +162,7 @@ def get_feature(
             if gradcam:
                 y_hat = cast(list[torch.Tensor] | tuple[torch.Tensor] | torch.Tensor, feature_extractor(x1).detach())
                 # mypy can't detect that gradcam is true only if we have a features_extractor
-                if is_vision_transformer(feature_extractor.features_extractor):  # type: ignore[union-attr]
+                if is_vision_transformer(cast(torch.nn.Module, feature_extractor.features_extractor)):  # type: ignore[union-attr]
                     grayscale_cam_low_res = grad_rollout(
                         input_tensor=x1, targets_list=y1
                     )  # TODO: We are using labels (y1) but it would be better to use preds