diff --git a/Makefile b/Makefile
index 2a9b668f64a7..a9ca1e91e2aa 100644
--- a/Makefile
+++ b/Makefile
@@ -1,20 +1,18 @@
-PYTHON_VERSION = 3.6.9
+PYTHON_VERSION = 3.9.13
 
-PIP = env/bin/pip
-PYTHON = env/bin/python
-VIRTUALENV = $(PYENV_ROOT)/versions/$(PYTHON_VERSION)/bin/virtualenv
+PIP = poetry run pip
+PYTHON = poetry run python
+
+VERSION := $(shell sed -rn 's/^__version__\s*=\s*"(.+)"/\1/p' yolov5/__init__.py)
 
 env:
-	pyenv install -s $(PYTHON_VERSION)  # make sure expected python version is available
-	$(PYENV_ROOT)/versions/$(PYTHON_VERSION)/bin/pip install virtualenv  # ensure virtualenv is installed for given python
-	if [ ! "$(shell $(PYTHON) -V)" = "Python $(PYTHON_VERSION)" ]; then \
-		echo "WARNING: python version mismatch => reset virtualenv" && rm -rf env/; \
-	fi
-	if [ ! -d env/ ]; then \
-		$(VIRTUALENV) env/; \
-	fi
+# make sure expected python version is available
+	PYTHON_CONFIGURE_OPTS=--enable-shared pyenv install -s $(PYTHON_VERSION)
+	pyenv local $(PYTHON_VERSION)
+	poetry install --with dev
 	$(PIP) install -qU pip
 
+
 init:
 	$(PIP) install -Ur requirements-dev.txt
 
@@ -34,17 +32,18 @@ test:
 	env/bin/pytest
 
 dist: clean
-	#check-manifest
-	$(PYTHON) setup.py sdist bdist_wheel
-	ls -l dist
-	env/bin/twine check dist/*
+	poetry check
+ifneq ($(VERSION),$(strip $(shell poetry version | cut -d " " -f 2 )))
+$(error Package and code versions mismatch, please try poetry install)
+endif
+	poetry build
 
 tag:
-	git tag -f -a -m "Auto-generated tag" `sed -rn 's/^__version__\s*=\s*"(.+)"/\1/p' yolov5/__init__.py`
+	git tag -f -a -m "Auto-generated tag" $(VERSION)
 	git push --tags --force
 
 release: clean dist tag
-	env/bin/twine upload dist/* --repository-url https://pypi.psycle.dev/
+	poetry publish -r psycle
 
 lint:
 	env/bin/black . --exclude '/(env)/'
diff --git a/README.md b/README.md
new file mode 100644
index 000000000000..cf88abf1a85b
--- /dev/null
+++ b/README.md
@@ -0,0 +1,57 @@
+# Fork of YOLOv5
+
+## About the package
+
+\# TODO
+
+## Package management
+
+### Makefile
+
+Poetry `~1.2` is required to run the Makefile.
+
+Install the environment:
+
+```bash
+make env
+```
+
+Build the source and wheel distribution:
+
+```bash
+make dist
+```
+
+Build and release the package to the private PyPI (requires to setup Poetry auth), and tag this commit:
+```bash
+make release
+```
+
+Remove all temporary files (cached Python code)
+```bash
+make clean
+```
+
+### Poetry
+
+This package is using Poetry. Here are some usefull commands.
+
+```bash
+# Install current package environment
+poetry install
+
+# Install current package environment with dev dependencies
+poetry install --with dev
+
+# Run a command inside the virtualenv
+poetry run my_command
+
+# Enter the virtualenv (exit with exit command or <CTRL+D>)
+poetry shell
+
+# Update dependances
+poetry lock
+
+# Refresh lock file without looking for updated packages
+poetry lock --no-update
+```
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 000000000000..73ab230bbeba
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,1067 @@
+[[package]]
+name = "astroid"
+version = "2.12.9"
+description = "An abstract syntax tree for Python with inference support."
+category = "dev"
+optional = false
+python-versions = ">=3.7.2"
+
+[package.dependencies]
+lazy-object-proxy = ">=1.4.0"
+typing-extensions = {version = ">=3.10", markers = "python_version < \"3.10\""}
+wrapt = [
+    {version = ">=1.11,<2", markers = "python_version < \"3.11\""},
+    {version = ">=1.14,<2", markers = "python_version >= \"3.11\""},
+]
+
+[[package]]
+name = "black"
+version = "22.8.0"
+description = "The uncompromising code formatter."
+category = "dev"
+optional = false
+python-versions = ">=3.6.2"
+
+[package.dependencies]
+click = ">=8.0.0"
+mypy-extensions = ">=0.4.3"
+pathspec = ">=0.9.0"
+platformdirs = ">=2"
+tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
+typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+colorama = ["colorama (>=0.4.3)"]
+d = ["aiohttp (>=3.7.4)"]
+jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
+uvloop = ["uvloop (>=0.15.2)"]
+
+[[package]]
+name = "certifi"
+version = "2022.6.15"
+description = "Python package for providing Mozilla's CA Bundle."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "charset-normalizer"
+version = "2.1.1"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+category = "main"
+optional = false
+python-versions = ">=3.6.0"
+
+[package.extras]
+unicode_backport = ["unicodedata2"]
+
+[[package]]
+name = "click"
+version = "8.1.3"
+description = "Composable command line interface toolkit"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[[package]]
+name = "colorama"
+version = "0.4.5"
+description = "Cross-platform colored terminal text."
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[[package]]
+name = "cycler"
+version = "0.11.0"
+description = "Composable style cycles"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "Cython"
+version = "0.29.32"
+description = "The Cython compiler for writing C extensions for the Python language."
+category = "main"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+
+[[package]]
+name = "dill"
+version = "0.3.5.1"
+description = "serialize all of python"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+
+[package.extras]
+graph = ["objgraph (>=1.7.2)"]
+
+[[package]]
+name = "fonttools"
+version = "4.37.1"
+description = "Tools to manipulate font files"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=14.0.0)", "xattr", "zopfli (>=0.1.4)"]
+graphite = ["lz4 (>=1.7.4.2)"]
+interpolatable = ["munkres", "scipy"]
+lxml = ["lxml (>=4.0,<5)"]
+pathops = ["skia-pathops (>=0.5.0)"]
+plot = ["matplotlib"]
+repacker = ["uharfbuzz (>=0.23.0)"]
+symfont = ["sympy"]
+type1 = ["xattr"]
+ufo = ["fs (>=2.2.0,<3)"]
+unicode = ["unicodedata2 (>=14.0.0)"]
+woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
+
+[[package]]
+name = "idna"
+version = "3.3"
+description = "Internationalized Domain Names in Applications (IDNA)"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
+[[package]]
+name = "isort"
+version = "5.10.1"
+description = "A Python utility / library to sort Python imports."
+category = "dev"
+optional = false
+python-versions = ">=3.6.1,<4.0"
+
+[package.extras]
+colors = ["colorama (>=0.4.3,<0.5.0)"]
+pipfile_deprecated_finder = ["pipreqs", "requirementslib"]
+plugins = ["setuptools"]
+requirements_deprecated_finder = ["pip-api", "pipreqs"]
+
+[[package]]
+name = "kiwisolver"
+version = "1.4.4"
+description = "A fast implementation of the Cassowary constraint solver"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "lazy-object-proxy"
+version = "1.7.1"
+description = "A fast and thorough lazy object proxy."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "matplotlib"
+version = "3.5.3"
+description = "Python plotting package"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+cycler = ">=0.10"
+fonttools = ">=4.22.0"
+kiwisolver = ">=1.0.1"
+numpy = ">=1.17"
+packaging = ">=20.0"
+pillow = ">=6.2.0"
+pyparsing = ">=2.2.1"
+python-dateutil = ">=2.7"
+setuptools_scm = ">=4,<7"
+
+[[package]]
+name = "mccabe"
+version = "0.7.0"
+description = "McCabe checker, plugin for flake8"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "mypy-extensions"
+version = "0.4.3"
+description = "Experimental type system extensions for programs checked with the mypy typechecker."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "numpy"
+version = "1.23.2"
+description = "NumPy is the fundamental package for array computing with Python."
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[[package]]
+name = "opencv-python"
+version = "4.6.0.66"
+description = "Wrapper package for OpenCV python bindings."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+numpy = [
+    {version = ">=1.21.2", markers = "python_version >= \"3.10\" or python_version >= \"3.6\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""},
+    {version = ">=1.19.3", markers = "python_version >= \"3.6\" and platform_system == \"Linux\" and platform_machine == \"aarch64\" or python_version >= \"3.9\""},
+    {version = ">=1.14.5", markers = "python_version >= \"3.7\""},
+    {version = ">=1.17.3", markers = "python_version >= \"3.8\""},
+]
+
+[[package]]
+name = "packaging"
+version = "21.3"
+description = "Core utilities for Python packages"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
+
+[[package]]
+name = "pathspec"
+version = "0.10.1"
+description = "Utility library for gitignore style pattern matching of file paths."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "Pillow"
+version = "9.2.0"
+description = "Python Imaging Library (Fork)"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"]
+tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "platformdirs"
+version = "2.5.2"
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx (>=4)", "sphinx-autodoc-typehints (>=1.12)"]
+test = ["appdirs (==1.4.4)", "pytest (>=6)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)"]
+
+[[package]]
+name = "pylint"
+version = "2.15.2"
+description = "python code static checker"
+category = "dev"
+optional = false
+python-versions = ">=3.7.2"
+
+[package.dependencies]
+astroid = ">=2.12.9,<=2.14.0-dev0"
+colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
+dill = ">=0.2"
+isort = ">=4.2.5,<6"
+mccabe = ">=0.6,<0.8"
+platformdirs = ">=2.2.0"
+tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
+tomlkit = ">=0.10.1"
+typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+spelling = ["pyenchant (>=3.2,<4.0)"]
+testutils = ["gitpython (>3)"]
+
+[[package]]
+name = "pyparsing"
+version = "3.0.9"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+category = "dev"
+optional = false
+python-versions = ">=3.6.8"
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
+[[package]]
+name = "python-dateutil"
+version = "2.8.2"
+description = "Extensions to the standard Python datetime module"
+category = "dev"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+name = "PyYAML"
+version = "6.0"
+description = "YAML parser and emitter for Python"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "requests"
+version = "2.28.1"
+description = "Python HTTP for Humans."
+category = "main"
+optional = false
+python-versions = ">=3.7, <4"
+
+[package.dependencies]
+certifi = ">=2017.4.17"
+charset-normalizer = ">=2,<3"
+idna = ">=2.5,<4"
+urllib3 = ">=1.21.1,<1.27"
+
+[package.extras]
+socks = ["PySocks (>=1.5.6,!=1.5.7)"]
+use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"]
+
+[[package]]
+name = "scipy"
+version = "1.6.1"
+description = "SciPy: Scientific Library for Python"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+numpy = ">=1.16.5"
+
+[[package]]
+name = "setuptools"
+version = "65.3.0"
+description = "Easily download, build, install, upgrade, and uninstall Python packages"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
+testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "mock", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+
+[[package]]
+name = "setuptools-scm"
+version = "6.4.2"
+description = "the blessed package to manage your versions by scm tags"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+packaging = ">=20.0"
+setuptools = "*"
+tomli = ">=1.0.0"
+
+[package.extras]
+test = ["pytest (>=6.2)", "virtualenv (>20)"]
+toml = ["setuptools (>=42)"]
+
+[[package]]
+name = "six"
+version = "1.16.0"
+description = "Python 2 and 3 compatibility utilities"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+
+[[package]]
+name = "tomli"
+version = "2.0.1"
+description = "A lil' TOML parser"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "tomlkit"
+version = "0.11.4"
+description = "Style preserving TOML library"
+category = "dev"
+optional = false
+python-versions = ">=3.6,<4.0"
+
+[[package]]
+name = "torch"
+version = "1.12.1"
+description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+
+[package.dependencies]
+typing-extensions = "*"
+
+[[package]]
+name = "torchvision"
+version = "0.13.1"
+description = "image and video datasets and models for torch deep learning"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+numpy = "*"
+pillow = ">=5.3.0,<8.3.0 || >=8.4.0"
+requests = "*"
+torch = "1.12.1"
+typing-extensions = "*"
+
+[package.extras]
+scipy = ["scipy"]
+
+[[package]]
+name = "tqdm"
+version = "4.64.1"
+description = "Fast, Extensible Progress Meter"
+category = "main"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+dev = ["py-make (>=0.1.0)", "twine", "wheel"]
+notebook = ["ipywidgets (>=6)"]
+slack = ["slack-sdk"]
+telegram = ["requests"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.3.0"
+description = "Backported and Experimental Type Hints for Python 3.7+"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "urllib3"
+version = "1.26.12"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, <4"
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)", "brotlipy (>=0.6.0)"]
+secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "ipaddress", "pyOpenSSL (>=0.14)", "urllib3-secure-extra"]
+socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
+
+[[package]]
+name = "wrapt"
+version = "1.14.1"
+description = "Module for decorators, wrappers and monkey patching."
+category = "dev"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
+
+[metadata]
+lock-version = "1.1"
+python-versions = "^3.9"
+content-hash = "e1c5b921b7d2edf1986e3e0bff44d1ea44f678e4a31f1f315d29ad37f78ce720"
+
+[metadata.files]
+astroid = [
+    {file = "astroid-2.12.9-py3-none-any.whl", hash = "sha256:27a22f40e45af6d362498647a0940e8ae9c35f71cb572a1b6f8f810122a11918"},
+    {file = "astroid-2.12.9.tar.gz", hash = "sha256:0dafbfcf4ebdecd3c8f6d742c9d9c88508229ca823d5c98ab872d964f3321e56"},
+]
+black = [
+    {file = "black-22.8.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:ce957f1d6b78a8a231b18e0dd2d94a33d2ba738cd88a7fe64f53f659eea49fdd"},
+    {file = "black-22.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:5107ea36b2b61917956d018bd25129baf9ad1125e39324a9b18248d362156a27"},
+    {file = "black-22.8.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:e8166b7bfe5dcb56d325385bd1d1e0f635f24aae14b3ae437102dedc0c186747"},
+    {file = "black-22.8.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd82842bb272297503cbec1a2600b6bfb338dae017186f8f215c8958f8acf869"},
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+    {file = "wrapt-1.14.1.tar.gz", hash = "sha256:380a85cf89e0e69b7cfbe2ea9f765f004ff419f34194018a6827ac0e3edfed4d"},
+]
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 000000000000..0c9962bfc437
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,34 @@
+[tool.poetry]
+name = "yolov5"
+version = "2.0.0"
+description = "Fork of yolov5"
+authors = ["Psycle Research <tech@psycle.io>"]
+license = "GPL-3.0"
+readme = "README.md"
+
+# poetry should auto-detect modules and package data using package name
+
+[tool.poetry.dependencies]
+python = "^3.9"
+Cython = ">=0.29.25"
+numpy = ">=1.19.1"
+PyYAML = "^6.0"
+tqdm = "^4.48.2"
+requests = "^2.26.0"
+Pillow = ">=7.2.0,<10.0"
+opencv-python = "^4.4.0.42"
+scipy = "^1.5.2"
+torch = "~1.12.0"
+torchvision = "~0.13.0"
+
+[tool.poetry.group.dev]
+optional = true
+
+[tool.poetry.group.dev.dependencies]
+matplotlib = ">=3.3.1"
+black = ">=22.8.0"
+pylint = "^2.13.9"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/requirements-dev.txt b/requirements-dev.txt
deleted file mode 100644
index 548f08399888..000000000000
--- a/requirements-dev.txt
+++ /dev/null
@@ -1,10 +0,0 @@
--r requirements.txt
-numpy==1.19.1
-opencv-python==4.4.0.42
-torch==1.8.0
-torchvision==0.9.0
-tensorboard==2.3.0
-check-manifest
-matplotlib==3.3.1
-twine
-scipy==1.5.2
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index deeb8b9b24bc..000000000000
--- a/requirements.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-Cython==0.29.25
-numpy>=1.19.1
-PyYAML==6.0
-tqdm==4.48.2
-requests==2.26.0
-Pillow>=7.2.0
-# opencv-python==4.4.0.42 # Only for x86
diff --git a/setup.py b/setup.py
deleted file mode 100644
index df5e287008f5..000000000000
--- a/setup.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import os
-from setuptools import setup
-from yolov5 import __version__
-
-
-def read(fname):
-    with open(os.path.join(os.path.dirname(__file__), fname), "rb") as fid:
-        return fid.read().decode("utf-8")
-
-
-req = read("requirements.txt").splitlines()
-
-requirements = req + ["setuptools"]
-
-setup(
-    name="yolov5",
-    version=__version__,
-    author="Psycle Research",
-    description="Fork of yolov5",
-    url="https://github.com/PsycleResearch/yolov5",
-    packages=[
-        "yolov5",
-        "yolov5.models",
-        "yolov5.utils",
-        "yolov5.utils.loggers",
-        "yolov5.utils.loggers.wandb",
-    ],
-    package_data={
-        "yolov5": ["data/*", "data/**/*", "models/*.yaml", "models/**/*.yaml"]
-    },
-    install_requires=requirements,
-    python_requires=">=3.6",
-)
diff --git a/yolov5/__init__.py b/yolov5/__init__.py
index 242989c9ade7..81d42031cbfa 100644
--- a/yolov5/__init__.py
+++ b/yolov5/__init__.py
@@ -4,7 +4,7 @@
 import os
 import sys
 
-__version__ = "1.2.1"
+__version__ = "2.0.0"
 
 yolov5_dir_path = os.path.dirname(os.path.realpath(__file__))
 sys.path.insert(0, yolov5_dir_path)
diff --git a/yolov5/models/experimental.py b/yolov5/models/experimental.py
index adb86c81fc06..00ae4ef2f13d 100644
--- a/yolov5/models/experimental.py
+++ b/yolov5/models/experimental.py
@@ -92,21 +92,22 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
     model = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
         ckpt = torch.load(attempt_download(w), map_location=map_location)  # load
-        if fuse:
-            model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model
-        else:
-            model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval())  # without layer fuse
+        ckpt = (ckpt['ema'] or ckpt['model']).float()  # FP32 model
+        model.append(ckpt.fuse().eval() if fuse else ckpt.eval())  # fused or un-fused model in eval mode
 
     # Compatibility updates
     for m in model.modules():
-        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
-            m.inplace = inplace  # pytorch 1.7.0 compatibility
-            if type(m) is Detect:
+        t = type(m)
+        if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
+            m.inplace = inplace  # torch 1.7.0 compatibility
+            if t is Detect:
                 if not isinstance(m.anchor_grid, list):  # new Detect Layer compatibility
                     delattr(m, 'anchor_grid')
                     setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
-        elif type(m) is Conv:
-            m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility
+        elif t is nn.Upsample:
+            m.recompute_scale_factor = None  # torch 1.11.0 compatibility
+        elif t is Conv:
+            m._non_persistent_buffers_set = set()  # torch 1.6.0 compatibility
 
     if len(model) == 1:
         return model[-1]  # return model
diff --git a/yolov5/utils/loss.py b/yolov5/utils/loss.py
index e8ce42ad994a..136f7ae2330b 100644
--- a/yolov5/utils/loss.py
+++ b/yolov5/utils/loss.py
@@ -10,7 +10,9 @@
 from utils.torch_utils import is_parallel
 
 
-def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
+def smooth_BCE(
+    eps=0.1,
+):  # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
     # return positive, negative label smoothing BCE targets
     return 1.0 - 0.5 * eps, 0.5 * eps
 
@@ -19,7 +21,9 @@ class BCEBlurWithLogitsLoss(nn.Module):
     # BCEwithLogitLoss() with reduced missing label effects.
     def __init__(self, alpha=0.05):
         super().__init__()
-        self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')  # must be nn.BCEWithLogitsLoss()
+        self.loss_fcn = nn.BCEWithLogitsLoss(
+            reduction="none"
+        )  # must be nn.BCEWithLogitsLoss()
         self.alpha = alpha
 
     def forward(self, pred, true):
@@ -40,7 +44,7 @@ def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
         self.gamma = gamma
         self.alpha = alpha
         self.reduction = loss_fcn.reduction
-        self.loss_fcn.reduction = 'none'  # required to apply FL to each element
+        self.loss_fcn.reduction = "none"  # required to apply FL to each element
 
     def forward(self, pred, true):
         loss = self.loss_fcn(pred, true)
@@ -54,9 +58,9 @@ def forward(self, pred, true):
         modulating_factor = (1.0 - p_t) ** self.gamma
         loss *= alpha_factor * modulating_factor
 
-        if self.reduction == 'mean':
+        if self.reduction == "mean":
             return loss.mean()
-        elif self.reduction == 'sum':
+        elif self.reduction == "sum":
             return loss.sum()
         else:  # 'none'
             return loss
@@ -70,7 +74,7 @@ def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
         self.gamma = gamma
         self.alpha = alpha
         self.reduction = loss_fcn.reduction
-        self.loss_fcn.reduction = 'none'  # required to apply FL to each element
+        self.loss_fcn.reduction = "none"  # required to apply FL to each element
 
     def forward(self, pred, true):
         loss = self.loss_fcn(pred, true)
@@ -80,9 +84,9 @@ def forward(self, pred, true):
         modulating_factor = torch.abs(true - pred_prob) ** self.gamma
         loss *= alpha_factor * modulating_factor
 
-        if self.reduction == 'mean':
+        if self.reduction == "mean":
             return loss.mean()
-        elif self.reduction == 'sum':
+        elif self.reduction == "sum":
             return loss.sum()
         else:  # 'none'
             return loss
@@ -96,27 +100,47 @@ def __init__(self, model, autobalance=False):
         h = model.hyp  # hyperparameters
 
         # Define criteria
-        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
-        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
+        BCEcls = nn.BCEWithLogitsLoss(
+            pos_weight=torch.tensor([h["cls_pw"]], device=device)
+        )
+        BCEobj = nn.BCEWithLogitsLoss(
+            pos_weight=torch.tensor([h["obj_pw"]], device=device)
+        )
 
         # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
-        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets
+        self.cp, self.cn = smooth_BCE(
+            eps=h.get("label_smoothing", 0.0)
+        )  # positive, negative BCE targets
 
         # Focal loss
-        g = h['fl_gamma']  # focal loss gamma
+        g = h["fl_gamma"]  # focal loss gamma
         if g > 0:
             BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
 
-        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
-        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7
+        det = (
+            model.module.model[-1] if is_parallel(model) else model.model[-1]
+        )  # Detect() module
+        self.balance = {3: [4.0, 1.0, 0.4]}.get(
+            det.nl, [4.0, 1.0, 0.25, 0.06, 0.02]
+        )  # P3-P7
         self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
-        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
-        for k in 'na', 'nc', 'nl', 'anchors':
+        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = (
+            BCEcls,
+            BCEobj,
+            1.0,
+            h,
+            autobalance,
+        )
+        for k in "na", "nc", "nl", "anchors":
             setattr(self, k, getattr(det, k))
 
     def __call__(self, p, targets):  # predictions, targets, model
         device = targets.device
-        lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
+        lcls, lbox, lobj = (
+            torch.zeros(1, device=device),
+            torch.zeros(1, device=device),
+            torch.zeros(1, device=device),
+        )
         tcls, tbox, indices, anchors = self.build_targets(p, targets)  # targets
 
         # Losses
@@ -129,17 +153,25 @@ def __call__(self, p, targets):  # predictions, targets, model
                 ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
 
                 # Regression
-                pxy = ps[:, :2].sigmoid() * 2. - 0.5
+                pxy = ps[:, :2].sigmoid() * 2.0 - 0.5
                 pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
                 pbox = torch.cat((pxy, pwh), 1)  # predicted box
-                iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
+                iou = bbox_iou(
+                    pbox.T, tbox[i], x1y1x2y2=False, CIoU=True
+                )  # iou(prediction, target)
                 lbox += (1.0 - iou).mean()  # iou loss
 
                 # Objectness
                 score_iou = iou.detach().clamp(0).type(tobj.dtype)
                 if self.sort_obj_iou:
                     sort_id = torch.argsort(score_iou)
-                    b, a, gj, gi, score_iou = b[sort_id], a[sort_id], gj[sort_id], gi[sort_id], score_iou[sort_id]
+                    b, a, gj, gi, score_iou = (
+                        b[sort_id],
+                        a[sort_id],
+                        gj[sort_id],
+                        gi[sort_id],
+                        score_iou[sort_id],
+                    )
                 tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * score_iou  # iou ratio
 
                 # Classification
@@ -155,13 +187,15 @@ def __call__(self, p, targets):  # predictions, targets, model
             obji = self.BCEobj(pi[..., 4], tobj)
             lobj += obji * self.balance[i]  # obj loss
             if self.autobalance:
-                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
+                self.balance[i] = (
+                    self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
+                )
 
         if self.autobalance:
             self.balance = [x / self.balance[self.ssi] for x in self.balance]
-        lbox *= self.hyp['box']
-        lobj *= self.hyp['obj']
-        lcls *= self.hyp['cls']
+        lbox *= self.hyp["box"]
+        lobj *= self.hyp["obj"]
+        lcls *= self.hyp["cls"]
         bs = tobj.shape[0]  # batch size
 
         return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
@@ -171,33 +205,47 @@ def build_targets(self, p, targets):
         na, nt = self.na, targets.shape[0]  # number of anchors, targets
         tcls, tbox, indices, anch = [], [], [], []
         gain = torch.ones(7, device=targets.device)  # normalized to gridspace gain
-        ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
-        targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices
+        ai = (
+            torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)
+        )  # same as .repeat_interleave(nt)
+        targets = torch.cat(
+            (targets.repeat(na, 1, 1), ai[:, :, None]), 2
+        )  # append anchor indices
 
         g = 0.5  # bias
-        off = torch.tensor([[0, 0],
-                            [1, 0], [0, 1], [-1, 0], [0, -1],  # j,k,l,m
-                            # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
-                            ], device=targets.device).float() * g  # offsets
+        off = (
+            torch.tensor(
+                [
+                    [0, 0],
+                    [1, 0],
+                    [0, 1],
+                    [-1, 0],
+                    [0, -1],  # j,k,l,m
+                    # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
+                ],
+                device=targets.device,
+            ).float()
+            * g
+        )  # offsets
 
         for i in range(self.nl):
-            anchors = self.anchors[i]
-            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
+            anchors, shape = self.anchors[i], p[i].shape
+            gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]]  # xyxy gain
 
             # Match targets to anchors
             t = targets * gain
             if nt:
                 # Matches
                 r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
+                j = torch.max(r, 1.0 / r).max(2)[0] < self.hyp["anchor_t"]  # compare
                 # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
                 t = t[j]  # filter
 
                 # Offsets
                 gxy = t[:, 2:4]  # grid xy
                 gxi = gain[[2, 3]] - gxy  # inverse
-                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
-                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
+                j, k = ((gxy % 1.0 < g) & (gxy > 1.0)).T
+                l, m = ((gxi % 1.0 < g) & (gxi > 1.0)).T
                 j = torch.stack((torch.ones_like(j), j, k, l, m))
                 t = t.repeat((5, 1, 1))[j]
                 offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
@@ -206,15 +254,16 @@ def build_targets(self, p, targets):
                 offsets = 0
 
             # Define
-            b, c = t[:, :2].long().T  # image, class
-            gxy = t[:, 2:4]  # grid xy
-            gwh = t[:, 4:6]  # grid wh
+            bc, gxy, gwh, a = t.chunk(4, 1)  # (image, class), grid xy, grid wh, anchors
+            a, (b, c) = a.long().view(-1), bc.long().T  # anchors, image, class
             gij = (gxy - offsets).long()
-            gi, gj = gij.T  # grid xy indices
+            gi, gj = gij.T  # grid indices
 
             # Append
-            a = t[:, 6].long()  # anchor indices
-            indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices
+            indices.append(
+                # image, anchor, grid
+                (b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))
+            )
             tbox.append(torch.cat((gxy - gij, gwh), 1))  # box
             anch.append(anchors[a])  # anchors
             tcls.append(c)  # class