diff --git a/.gitignore b/.gitignore
index 435f4d5..775fd72 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,4 @@
 __pycache__/
 
 checkpoints/
+wandb/
diff --git a/poetry.lock b/poetry.lock
index 32d039d..a5fd360 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -19,6 +19,36 @@ develop = ["pytest", "imgaug (>=0.4.0)"]
 imgaug = ["imgaug (>=0.4.0)"]
 tests = ["pytest"]
 
+[[package]]
+name = "appnope"
+version = "0.1.3"
+description = "Disable App Nap on macOS >= 10.9"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "asttokens"
+version = "2.0.5"
+description = "Annotate AST trees with source code positions"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+six = "*"
+
+[package.extras]
+test = ["astroid", "pytest"]
+
+[[package]]
+name = "backcall"
+version = "0.2.0"
+description = "Specifications for callback functions passed in to an API"
+category = "dev"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "black"
 version = "22.3.0"
@@ -49,6 +79,17 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "cffi"
+version = "1.15.0"
+description = "Foreign Function Interface for Python calling C code."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+pycparser = "*"
+
 [[package]]
 name = "charset-normalizer"
 version = "2.0.12"
@@ -87,6 +128,22 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "debugpy"
+version = "1.6.0"
+description = "An implementation of the Debug Adapter Protocol for Python"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "decorator"
+version = "5.1.1"
+description = "Decorators for Humans"
+category = "dev"
+optional = false
+python-versions = ">=3.5"
+
 [[package]]
 name = "docker-pycreds"
 version = "0.4.0"
@@ -98,6 +155,22 @@ python-versions = "*"
 [package.dependencies]
 six = ">=1.4.0"
 
+[[package]]
+name = "entrypoints"
+version = "0.4"
+description = "Discover and load entry points from installed packages."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "executing"
+version = "0.8.3"
+description = "Get the currently executing AST node of a frame, and other information"
+category = "dev"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "fonttools"
 version = "4.33.3"
@@ -179,11 +252,70 @@ pyav = ["av"]
 test = ["invoke", "pytest", "pytest-cov", "fsspec"]
 tifffile = ["tifffile"]
 
+[[package]]
+name = "ipykernel"
+version = "6.15.0"
+description = "IPython Kernel for Jupyter"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+appnope = {version = "*", markers = "platform_system == \"Darwin\""}
+debugpy = ">=1.0"
+ipython = ">=7.23.1"
+jupyter-client = ">=6.1.12"
+matplotlib-inline = ">=0.1"
+nest-asyncio = "*"
+packaging = "*"
+psutil = "*"
+pyzmq = ">=17"
+tornado = ">=6.1"
+traitlets = ">=5.1.0"
+
+[package.extras]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest-cov", "pytest-timeout", "pytest (>=6.0)"]
+
+[[package]]
+name = "ipython"
+version = "8.4.0"
+description = "IPython: Productive Interactive Computing"
+category = "dev"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+appnope = {version = "*", markers = "sys_platform == \"darwin\""}
+backcall = "*"
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+decorator = "*"
+jedi = ">=0.16"
+matplotlib-inline = "*"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
+pickleshare = "*"
+prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
+pygments = ">=2.4.0"
+stack-data = "*"
+traitlets = ">=5"
+
+[package.extras]
+all = ["black", "Sphinx (>=1.3)", "ipykernel", "nbconvert", "nbformat", "ipywidgets", "notebook", "ipyparallel", "qtconsole", "pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "numpy (>=1.19)", "pandas", "trio"]
+black = ["black"]
+doc = ["Sphinx (>=1.3)"]
+kernel = ["ipykernel"]
+nbconvert = ["nbconvert"]
+nbformat = ["nbformat"]
+notebook = ["ipywidgets", "notebook"]
+parallel = ["ipyparallel"]
+qtconsole = ["qtconsole"]
+test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
+test_extra = ["pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.19)", "pandas", "trio"]
+
 [[package]]
 name = "isort"
 version = "5.10.1"
 description = "A Python utility / library to sort Python imports."
-category = "main"
+category = "dev"
 optional = false
 python-versions = ">=3.6.1,<4.0"
 
@@ -193,6 +325,21 @@ requirements_deprecated_finder = ["pipreqs", "pip-api"]
 colors = ["colorama (>=0.4.3,<0.5.0)"]
 plugins = ["setuptools"]
 
+[[package]]
+name = "jedi"
+version = "0.18.1"
+description = "An autocompletion tool for Python that can be used for text editors."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+parso = ">=0.8.0,<0.9.0"
+
+[package.extras]
+qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
+testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<7.0.0)"]
+
 [[package]]
 name = "joblib"
 version = "1.1.0"
@@ -201,6 +348,42 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "jupyter-client"
+version = "7.3.4"
+description = "Jupyter protocol implementation and client libraries"
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+entrypoints = "*"
+jupyter-core = ">=4.9.2"
+nest-asyncio = ">=1.5.4"
+python-dateutil = ">=2.8.2"
+pyzmq = ">=23.0"
+tornado = ">=6.0"
+traitlets = "*"
+
+[package.extras]
+doc = ["ipykernel", "myst-parser", "sphinx-rtd-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt"]
+test = ["codecov", "coverage", "ipykernel (>=6.5)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "jupyter-core"
+version = "4.10.0"
+description = "Jupyter core package. A base package on which Jupyter projects rely."
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
+traitlets = "*"
+
+[package.extras]
+test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
+
 [[package]]
 name = "kiwisolver"
 version = "1.4.3"
@@ -228,6 +411,17 @@ pyparsing = ">=2.2.1"
 python-dateutil = ">=2.7"
 setuptools_scm = ">=4"
 
+[[package]]
+name = "matplotlib-inline"
+version = "0.1.3"
+description = "Inline Matplotlib backend for Jupyter"
+category = "dev"
+optional = false
+python-versions = ">=3.5"
+
+[package.dependencies]
+traitlets = "*"
+
 [[package]]
 name = "mypy-extensions"
 version = "0.4.3"
@@ -236,6 +430,14 @@ category = "dev"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "nest-asyncio"
+version = "1.5.5"
+description = "Patch asyncio to allow nested event loops"
+category = "dev"
+optional = false
+python-versions = ">=3.5"
+
 [[package]]
 name = "networkx"
 version = "2.8.4"
@@ -286,6 +488,18 @@ python-versions = ">=3.6"
 [package.dependencies]
 pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
 
+[[package]]
+name = "parso"
+version = "0.8.3"
+description = "A Python Parser"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
+testing = ["docopt", "pytest (<6.0.0)"]
+
 [[package]]
 name = "pathspec"
 version = "0.9.0"
@@ -302,6 +516,25 @@ category = "main"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "pexpect"
+version = "4.8.0"
+description = "Pexpect allows easy control of interactive console applications."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+ptyprocess = ">=0.5"
+
+[[package]]
+name = "pickleshare"
+version = "0.7.5"
+description = "Tiny 'shelve'-like database with concurrency support"
+category = "dev"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "pillow"
 version = "9.1.1"
@@ -340,6 +573,17 @@ six = "*"
 [package.extras]
 test = ["pytest (>=2.7.3)", "pytest-cov", "coveralls", "futures", "pytest-benchmark", "mock"]
 
+[[package]]
+name = "prompt-toolkit"
+version = "3.0.30"
+description = "Library for building powerful interactive command lines in Python"
+category = "dev"
+optional = false
+python-versions = ">=3.6.2"
+
+[package.dependencies]
+wcwidth = "*"
+
 [[package]]
 name = "protobuf"
 version = "3.20.1"
@@ -359,6 +603,49 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 [package.extras]
 test = ["ipaddress", "mock", "enum34", "pywin32", "wmi"]
 
+[[package]]
+name = "ptyprocess"
+version = "0.7.0"
+description = "Run a subprocess in a pseudo terminal"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "pure-eval"
+version = "0.2.2"
+description = "Safely evaluate AST nodes without side effects"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.extras]
+tests = ["pytest"]
+
+[[package]]
+name = "py"
+version = "1.11.0"
+description = "library with cross-python path, ini-parsing, io, code, log facilities"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[[package]]
+name = "pycparser"
+version = "2.21"
+description = "C parser in Python"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
+[[package]]
+name = "pygments"
+version = "2.12.0"
+description = "Pygments is a syntax highlighting package written in Python."
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "pyparsing"
 version = "3.0.9"
@@ -392,6 +679,14 @@ python-versions = ">=3.7"
 [package.dependencies]
 numpy = ">=1.17.3"
 
+[[package]]
+name = "pywin32"
+version = "304"
+description = "Python for Window Extensions"
+category = "dev"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "pyyaml"
 version = "6.0"
@@ -400,6 +695,18 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "pyzmq"
+version = "23.2.0"
+description = "Python bindings for 0MQ"
+category = "dev"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+cffi = {version = "*", markers = "implementation_name == \"pypy\""}
+py = {version = "*", markers = "implementation_name == \"pypy\""}
+
 [[package]]
 name = "qudida"
 version = "0.0.4"
@@ -569,6 +876,22 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "stack-data"
+version = "0.3.0"
+description = "Extract data from python stack frames and tracebacks for informative displays"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+asttokens = "*"
+executing = "*"
+pure-eval = "*"
+
+[package.extras]
+tests = ["pytest", "typeguard", "pygments", "littleutils", "cython"]
+
 [[package]]
 name = "threadpoolctl"
 version = "3.1.0"
@@ -628,6 +951,14 @@ typing-extensions = "*"
 [package.extras]
 scipy = ["scipy"]
 
+[[package]]
+name = "tornado"
+version = "6.1"
+description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+category = "dev"
+optional = false
+python-versions = ">= 3.5"
+
 [[package]]
 name = "tqdm"
 version = "4.64.0"
@@ -645,6 +976,17 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "traitlets"
+version = "5.3.0"
+description = ""
+category = "dev"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+test = ["pre-commit", "pytest"]
+
 [[package]]
 name = "typing-extensions"
 version = "4.2.0"
@@ -699,16 +1041,36 @@ launch = ["nbconvert", "nbformat", "chardet", "iso8601", "typing-extensions", "b
 media = ["numpy", "moviepy", "pillow", "bokeh", "soundfile", "plotly", "rdkit-pypi"]
 sweeps = ["sweeps (>=0.1.0)"]
 
+[[package]]
+name = "wcwidth"
+version = "0.2.5"
+description = "Measures the displayed width of unicode strings in a terminal"
+category = "dev"
+optional = false
+python-versions = "*"
+
 [metadata]
 lock-version = "1.1"
 python-versions = ">=3.8,<3.11"
-content-hash = "c176396881b9758ed2f1e14e68ca042ea676d349ff4e8100cae92ca8220046be"
+content-hash = "9f0407f3a873baf75816add27d7eeb538a83f688e470743c0d4a5660dc4470c0"
 
 [metadata.files]
 albumentations = [
     {file = "albumentations-1.2.0-py3-none-any.whl", hash = "sha256:19d42158529a5382d9049258b52c35fa7d84b97b248c50d5b9bfa0948005ba4a"},
     {file = "albumentations-1.2.0.tar.gz", hash = "sha256:44efc1f031bde6c167a16a45e1d49885cc02ed595d189072c98bdaae077daeac"},
 ]
+appnope = [
+    {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
+    {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
+]
+asttokens = [
+    {file = "asttokens-2.0.5-py2.py3-none-any.whl", hash = "sha256:0844691e88552595a6f4a4281a9f7f79b8dd45ca4ccea82e5e05b4bbdb76705c"},
+    {file = "asttokens-2.0.5.tar.gz", hash = "sha256:9a54c114f02c7a9480d56550932546a3f1fe71d8a02f1bc7ccd0ee3ee35cf4d5"},
+]
+backcall = [
+    {file = "backcall-0.2.0-py2.py3-none-any.whl", hash = "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"},
+    {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
+]
 black = [
     {file = "black-22.3.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:2497f9c2386572e28921fa8bec7be3e51de6801f7459dffd6e62492531c47e09"},
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@@ -738,6 +1100,58 @@ certifi = [
     {file = "certifi-2022.6.15-py3-none-any.whl", hash = "sha256:fe86415d55e84719d75f8b69414f6438ac3547d2078ab91b67e779ef69378412"},
     {file = "certifi-2022.6.15.tar.gz", hash = "sha256:84c85a9078b11105f04f3036a9482ae10e4621616db313fe045dd24743a0820d"},
 ]
+cffi = [
+    {file = "cffi-1.15.0-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:c2502a1a03b6312837279c8c1bd3ebedf6c12c4228ddbad40912d671ccc8a962"},
+    {file = "cffi-1.15.0-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:23cfe892bd5dd8941608f93348c0737e369e51c100d03718f108bf1add7bd6d0"},
+    {file = "cffi-1.15.0-cp27-cp27m-manylinux1_x86_64.whl", hash = "sha256:41d45de54cd277a7878919867c0f08b0cf817605e4eb94093e7516505d3c8d14"},
+    {file = "cffi-1.15.0-cp27-cp27m-win32.whl", hash = "sha256:4a306fa632e8f0928956a41fa8e1d6243c71e7eb59ffbd165fc0b41e316b2474"},
+    {file = "cffi-1.15.0-cp27-cp27m-win_amd64.whl", hash = "sha256:e7022a66d9b55e93e1a845d8c9eba2a1bebd4966cd8bfc25d9cd07d515b33fa6"},
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+    {file = "pyzmq-23.2.0-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:21552624ce69e69f7924f413b802b1fb554f4c0497f837810e429faa1cd4f163"},
+    {file = "pyzmq-23.2.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c616893a577e9d6773a3836732fd7e2a729157a108b8fccd31c87512fa01671a"},
+    {file = "pyzmq-23.2.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:ce4f71e17fa849de41a06109030d3f6815fcc33338bf98dd0dde6d456d33c929"},
+    {file = "pyzmq-23.2.0.tar.gz", hash = "sha256:a51f12a8719aad9dcfb55d456022f16b90abc8dde7d3ca93ce3120b40e3fa169"},
+]
 qudida = [
     {file = "qudida-0.0.4-py3-none-any.whl", hash = "sha256:4519714c40cd0f2e6c51e1735edae8f8b19f4efe1f33be13e9d644ca5f736dd6"},
     {file = "qudida-0.0.4.tar.gz", hash = "sha256:db198e2887ab0c9aa0023e565afbff41dfb76b361f85fd5e13f780d75ba18cc8"},
@@ -1269,6 +1854,10 @@ smmap = [
     {file = "smmap-5.0.0-py3-none-any.whl", hash = "sha256:2aba19d6a040e78d8b09de5c57e96207b09ed71d8e55ce0959eeee6c8e190d94"},
     {file = "smmap-5.0.0.tar.gz", hash = "sha256:c840e62059cd3be204b0c9c9f74be2c09d5648eddd4580d9314c3ecde0b30936"},
 ]
+stack-data = [
+    {file = "stack_data-0.3.0-py3-none-any.whl", hash = "sha256:aa1d52d14d09c7a9a12bb740e6bdfffe0f5e8f4f9218d85e7c73a8c37f7ae38d"},
+    {file = "stack_data-0.3.0.tar.gz", hash = "sha256:77bec1402dcd0987e9022326473fdbcc767304892a533ed8c29888dacb7dddbc"},
+]
 threadpoolctl = [
     {file = "threadpoolctl-3.1.0-py3-none-any.whl", hash = "sha256:8b99adda265feb6773280df41eece7b2e6561b772d21ffd52e372f999024907b"},
     {file = "threadpoolctl-3.1.0.tar.gz", hash = "sha256:a335baacfaa4400ae1f0d8e3a58d6674d2f8828e3716bb2802c44955ad391380"},
@@ -1323,10 +1912,57 @@ torchvision = [
     {file = "torchvision-0.12.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:b93a767f44e3933cb3b01a6fe9727db54590f57b7dac09d5aaf15966c6c151dd"},
     {file = "torchvision-0.12.0-cp39-cp39-win_amd64.whl", hash = "sha256:edab05f7ba9f648c00435b384ffdbd7bde79a3b8ea893813fb50f6ccf28b1e76"},
 ]
+tornado = [
+    {file = "tornado-6.1-cp35-cp35m-macosx_10_9_x86_64.whl", hash = "sha256:d371e811d6b156d82aa5f9a4e08b58debf97c302a35714f6f45e35139c332e32"},
+    {file = "tornado-6.1-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:0d321a39c36e5f2c4ff12b4ed58d41390460f798422c4504e09eb5678e09998c"},
+    {file = "tornado-6.1-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:9de9e5188a782be6b1ce866e8a51bc76a0fbaa0e16613823fc38e4fc2556ad05"},
+    {file = "tornado-6.1-cp35-cp35m-manylinux2010_i686.whl", hash = "sha256:61b32d06ae8a036a6607805e6720ef00a3c98207038444ba7fd3d169cd998910"},
+    {file = "tornado-6.1-cp35-cp35m-manylinux2010_x86_64.whl", hash = "sha256:3e63498f680547ed24d2c71e6497f24bca791aca2fe116dbc2bd0ac7f191691b"},
+    {file = "tornado-6.1-cp35-cp35m-manylinux2014_aarch64.whl", hash = "sha256:6c77c9937962577a6a76917845d06af6ab9197702a42e1346d8ae2e76b5e3675"},
+    {file = "tornado-6.1-cp35-cp35m-win32.whl", hash = "sha256:6286efab1ed6e74b7028327365cf7346b1d777d63ab30e21a0f4d5b275fc17d5"},
+    {file = "tornado-6.1-cp35-cp35m-win_amd64.whl", hash = "sha256:fa2ba70284fa42c2a5ecb35e322e68823288a4251f9ba9cc77be04ae15eada68"},
+    {file = "tornado-6.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:0a00ff4561e2929a2c37ce706cb8233b7907e0cdc22eab98888aca5dd3775feb"},
+    {file = "tornado-6.1-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:748290bf9112b581c525e6e6d3820621ff020ed95af6f17fedef416b27ed564c"},
+    {file = "tornado-6.1-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:e385b637ac3acaae8022e7e47dfa7b83d3620e432e3ecb9a3f7f58f150e50921"},
+    {file = "tornado-6.1-cp36-cp36m-manylinux2010_i686.whl", hash = "sha256:25ad220258349a12ae87ede08a7b04aca51237721f63b1808d39bdb4b2164558"},
+    {file = "tornado-6.1-cp36-cp36m-manylinux2010_x86_64.whl", hash = "sha256:65d98939f1a2e74b58839f8c4dab3b6b3c1ce84972ae712be02845e65391ac7c"},
+    {file = "tornado-6.1-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:e519d64089b0876c7b467274468709dadf11e41d65f63bba207e04217f47c085"},
+    {file = "tornado-6.1-cp36-cp36m-win32.whl", hash = "sha256:b87936fd2c317b6ee08a5741ea06b9d11a6074ef4cc42e031bc6403f82a32575"},
+    {file = "tornado-6.1-cp36-cp36m-win_amd64.whl", hash = "sha256:cc0ee35043162abbf717b7df924597ade8e5395e7b66d18270116f8745ceb795"},
+    {file = "tornado-6.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:7250a3fa399f08ec9cb3f7b1b987955d17e044f1ade821b32e5f435130250d7f"},
+    {file = "tornado-6.1-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:ed3ad863b1b40cd1d4bd21e7498329ccaece75db5a5bf58cd3c9f130843e7102"},
+    {file = "tornado-6.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:dcef026f608f678c118779cd6591c8af6e9b4155c44e0d1bc0c87c036fb8c8c4"},
+    {file = "tornado-6.1-cp37-cp37m-manylinux2010_i686.whl", hash = "sha256:70dec29e8ac485dbf57481baee40781c63e381bebea080991893cd297742b8fd"},
+    {file = "tornado-6.1-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:d3f7594930c423fd9f5d1a76bee85a2c36fd8b4b16921cae7e965f22575e9c01"},
+    {file = "tornado-6.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:3447475585bae2e77ecb832fc0300c3695516a47d46cefa0528181a34c5b9d3d"},
+    {file = "tornado-6.1-cp37-cp37m-win32.whl", hash = "sha256:e7229e60ac41a1202444497ddde70a48d33909e484f96eb0da9baf8dc68541df"},
+    {file = "tornado-6.1-cp37-cp37m-win_amd64.whl", hash = "sha256:cb5ec8eead331e3bb4ce8066cf06d2dfef1bfb1b2a73082dfe8a161301b76e37"},
+    {file = "tornado-6.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:20241b3cb4f425e971cb0a8e4ffc9b0a861530ae3c52f2b0434e6c1b57e9fd95"},
+    {file = "tornado-6.1-cp38-cp38-manylinux1_i686.whl", hash = "sha256:c77da1263aa361938476f04c4b6c8916001b90b2c2fdd92d8d535e1af48fba5a"},
+    {file = "tornado-6.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:fba85b6cd9c39be262fcd23865652920832b61583de2a2ca907dbd8e8a8c81e5"},
+    {file = "tornado-6.1-cp38-cp38-manylinux2010_i686.whl", hash = "sha256:1e8225a1070cd8eec59a996c43229fe8f95689cb16e552d130b9793cb570a288"},
+    {file = "tornado-6.1-cp38-cp38-manylinux2010_x86_64.whl", hash = "sha256:d14d30e7f46a0476efb0deb5b61343b1526f73ebb5ed84f23dc794bdb88f9d9f"},
+    {file = "tornado-6.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:8f959b26f2634a091bb42241c3ed8d3cedb506e7c27b8dd5c7b9f745318ddbb6"},
+    {file = "tornado-6.1-cp38-cp38-win32.whl", hash = "sha256:34ca2dac9e4d7afb0bed4677512e36a52f09caa6fded70b4e3e1c89dbd92c326"},
+    {file = "tornado-6.1-cp38-cp38-win_amd64.whl", hash = "sha256:6196a5c39286cc37c024cd78834fb9345e464525d8991c21e908cc046d1cc02c"},
+    {file = "tornado-6.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:f0ba29bafd8e7e22920567ce0d232c26d4d47c8b5cf4ed7b562b5db39fa199c5"},
+    {file = "tornado-6.1-cp39-cp39-manylinux1_i686.whl", hash = "sha256:33892118b165401f291070100d6d09359ca74addda679b60390b09f8ef325ffe"},
+    {file = "tornado-6.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:7da13da6f985aab7f6f28debab00c67ff9cbacd588e8477034c0652ac141feea"},
+    {file = "tornado-6.1-cp39-cp39-manylinux2010_i686.whl", hash = "sha256:e0791ac58d91ac58f694d8d2957884df8e4e2f6687cdf367ef7eb7497f79eaa2"},
+    {file = "tornado-6.1-cp39-cp39-manylinux2010_x86_64.whl", hash = "sha256:66324e4e1beede9ac79e60f88de548da58b1f8ab4b2f1354d8375774f997e6c0"},
+    {file = "tornado-6.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:a48900ecea1cbb71b8c71c620dee15b62f85f7c14189bdeee54966fbd9a0c5bd"},
+    {file = "tornado-6.1-cp39-cp39-win32.whl", hash = "sha256:d3d20ea5782ba63ed13bc2b8c291a053c8d807a8fa927d941bd718468f7b950c"},
+    {file = "tornado-6.1-cp39-cp39-win_amd64.whl", hash = "sha256:548430be2740e327b3fe0201abe471f314741efcb0067ec4f2d7dcfb4825f3e4"},
+    {file = "tornado-6.1.tar.gz", hash = "sha256:33c6e81d7bd55b468d2e793517c909b139960b6c790a60b7991b9b6b76fb9791"},
+]
 tqdm = [
     {file = "tqdm-4.64.0-py2.py3-none-any.whl", hash = "sha256:74a2cdefe14d11442cedf3ba4e21a3b84ff9a2dbdc6cfae2c34addb2a14a5ea6"},
     {file = "tqdm-4.64.0.tar.gz", hash = "sha256:40be55d30e200777a307a7585aee69e4eabb46b4ec6a4b4a5f2d9f11e7d5408d"},
 ]
+traitlets = [
+    {file = "traitlets-5.3.0-py3-none-any.whl", hash = "sha256:65fa18961659635933100db8ca120ef6220555286949774b9cfc106f941d1c7a"},
+    {file = "traitlets-5.3.0.tar.gz", hash = "sha256:0bb9f1f9f017aa8ec187d8b1b2a7a6626a2a1d877116baba52a129bfa124f8e2"},
+]
 typing-extensions = [
     {file = "typing_extensions-4.2.0-py3-none-any.whl", hash = "sha256:6657594ee297170d19f67d55c05852a874e7eb634f4f753dbd667855e07c1708"},
     {file = "typing_extensions-4.2.0.tar.gz", hash = "sha256:f1c24655a0da0d1b67f07e17a5e6b2a105894e6824b92096378bb3668ef02376"},
@@ -1339,3 +1975,7 @@ wandb = [
     {file = "wandb-0.12.19-py2.py3-none-any.whl", hash = "sha256:ff344e35a31a0d6ccfccaf0390895e4267392f994e395d16b348d7d2b2f67a18"},
     {file = "wandb-0.12.19.tar.gz", hash = "sha256:72c54a918e2453ffa661934faf6c3c8b39269423daba0999b9919bcaef3ae320"},
 ]
+wcwidth = [
+    {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
+    {file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
+]
diff --git a/pyproject.toml b/pyproject.toml
index d89bfc1..6705fb1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,7 @@ wandb = "^0.12.19"
 [tool.poetry.dev-dependencies]
 black = "^22.3.0"
 isort = "^5.10.1"
+ipykernel = "^6.15.0"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
diff --git a/src/train.py b/src/train.py
index 690273e..969a43a 100644
--- a/src/train.py
+++ b/src/train.py
@@ -2,22 +2,28 @@ import argparse
 import logging
 from pathlib import Path
 
+import albumentations as A
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import wandb
+from albumentations.pytorch import ToTensorV2
 from torch import optim
-from torch.utils.data import DataLoader, random_split
+from torch.utils.data import DataLoader
 from tqdm import tqdm
 
+import wandb
 from evaluate import evaluate
-from src.utils.dataset import BasicDataset, CarvanaDataset
-from unet import UNet
+from src.utils.dataset import SphereDataset
 from src.utils.dice import dice_loss
+from unet import UNet
+from utils.paste import RandomPaste
 
-dir_img = Path("./data/imgs/")
-dir_mask = Path("./data/masks/")
-dir_checkpoint = Path("./checkpoints/")
+CHECKPOINT_DIR = Path("./checkpoints/")
+DIR_TRAIN_IMG = Path("/home/lilian/data_disk/lfainsin/train2017")
+DIR_VALID_IMG = Path("/home/lilian/data_disk/lfainsin/val2017/")
+# DIR_VALID_MASK = Path("/home/lilian/data_disk/lfainsin/val2017mask/")
+DIR_SPHERE_IMG = Path("/home/lilian/data_disk/lfainsin/spheres/Images/")
+DIR_SPHERE_MASK = Path("/home/lilian/data_disk/lfainsin/spheres/Masks/")
 
 
 def train_net(
@@ -27,37 +33,48 @@ def train_net(
     batch_size: int = 1,
     learning_rate: float = 1e-5,
     save_checkpoint: bool = True,
-    img_scale: float = 0.5,
     amp: bool = False,
 ):
-    # 1. Create dataset
-    try:
-        dataset = CarvanaDataset(dir_img, dir_mask, img_scale)
-    except (AssertionError, RuntimeError):
-        dataset = BasicDataset(dir_img, dir_mask, img_scale)
+    # 1. Create transforms
+    tf_train = A.Compose(
+        [
+            A.Flip(),
+            A.ColorJitter(),
+            RandomPaste(5, 0.2, DIR_SPHERE_IMG, DIR_SPHERE_MASK),
+            A.ISONoise(),
+            A.ToFloat(max_value=255),
+            A.pytorch.ToTensorV2(),
+        ],
+    )
 
-    # 2. Split into train / validation partitions
-    n_val = int(len(dataset) * val_percent)
-    n_train = len(dataset) - n_val
-    train_set, val_set = random_split(dataset, [n_train, n_val], generator=torch.Generator().manual_seed(0))
+    tf_valid = A.Compose(
+        [
+            RandomPaste(5, 0.2, DIR_SPHERE_IMG, DIR_SPHERE_MASK),
+            A.ToFloat(max_value=255),
+            ToTensorV2(),
+        ],
+    )
+
+    # 2. Create datasets
+    ds_train = SphereDataset(images_dir=DIR_TRAIN_IMG, transform=tf_train)
+    # ds_valid = SphereDataset(images_dir=DIR_VALID_IMG, masks_dir=DIR_VALID_MASK, transform=tf_valid)
+    ds_valid = SphereDataset(images_dir=DIR_VALID_IMG, transform=tf_valid)
 
     # 3. Create data loaders
     loader_args = dict(batch_size=batch_size, num_workers=4, pin_memory=True)
-    train_loader = DataLoader(train_set, shuffle=True, **loader_args)
-    val_loader = DataLoader(val_set, shuffle=False, drop_last=True, **loader_args)
+    train_loader = DataLoader(ds_train, shuffle=True, **loader_args)
+    val_loader = DataLoader(ds_valid, shuffle=False, drop_last=True, **loader_args)
 
     # (Initialize logging)
-    experiment = wandb.init(project="U-Net", resume="allow", anonymous="must")
-    experiment.config.update(
-        dict(
+    experiment = wandb.init(
+        project="U-Net",
+        config=dict(
             epochs=epochs,
             batch_size=batch_size,
             learning_rate=learning_rate,
-            val_percent=val_percent,
             save_checkpoint=save_checkpoint,
-            img_scale=img_scale,
             amp=amp,
-        )
+        ),
     )
 
     logging.info(
@@ -65,13 +82,12 @@ def train_net(
         Epochs:          {epochs}
         Batch size:      {batch_size}
         Learning rate:   {learning_rate}
-        Training size:   {n_train}
-        Validation size: {n_val}
+        Training size:   {len(ds_train)}
+        Validation size: {len(ds_valid)}
         Checkpoints:     {save_checkpoint}
         Device:          {device.type}
-        Images scaling:  {img_scale}
         Mixed Precision: {amp}
-    """
+        """
     )
 
     # 4. Set up the optimizer, the loss, the learning rate scheduler and the loss scaling for AMP
@@ -85,7 +101,8 @@ def train_net(
     for epoch in range(1, epochs + 1):
         net.train()
         epoch_loss = 0
-        with tqdm(total=n_train, desc=f"Epoch {epoch}/{epochs}", unit="img") as pbar:
+
+        with tqdm(total=len(ds_train), desc=f"Epoch {epoch}/{epochs}", unit="img") as pbar:
             for batch in train_loader:
                 images = batch["image"]
                 true_masks = batch["mask"]
@@ -119,7 +136,7 @@ def train_net(
                 pbar.set_postfix(**{"loss (batch)": loss.item()})
 
                 # Evaluation round
-                division_step = n_train // (10 * batch_size)
+                division_step = len(ds_train) // (10 * batch_size)
                 if division_step > 0:
                     if global_step % division_step == 0:
                         histograms = {}
@@ -150,8 +167,8 @@ def train_net(
                         )
 
         if save_checkpoint:
-            Path(dir_checkpoint).mkdir(parents=True, exist_ok=True)
-            torch.save(net.state_dict(), str(dir_checkpoint / "checkpoint_epoch{}.pth".format(epoch)))
+            Path(CHECKPOINT_DIR).mkdir(parents=True, exist_ok=True)
+            torch.save(net.state_dict(), str(CHECKPOINT_DIR / "checkpoint_epoch{}.pth".format(epoch)))
             logging.info(f"Checkpoint {epoch} saved!")
 
 
@@ -173,7 +190,7 @@ def get_args():
         dest="batch_size",
         metavar="B",
         type=int,
-        default=1,
+        default=32,
         help="Batch size",
     )
     parser.add_argument(
@@ -192,13 +209,6 @@ def get_args():
         default=False,
         help="Load model from a .pth file",
     )
-    parser.add_argument(
-        "--scale",
-        "-s",
-        type=float,
-        default=0.5,
-        help="Downscaling factor of the images",
-    )
     parser.add_argument(
         "--amp",
         action="store_true",
@@ -220,16 +230,16 @@ if __name__ == "__main__":
     args = get_args()
 
     logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
+
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     logging.info(f"Using device {device}")
 
     net = UNet(n_channels=3, n_classes=args.classes)
 
     logging.info(
-        f"""
-        Network:\n
-        \t{net.n_channels} input channels\n
-        \t{net.n_classes} output channels (classes)\n
+        f"""Network:
+        \t{net.n_channels} input channels
+        \t{net.n_classes} output channels (classes)
         """
     )
 
@@ -238,6 +248,7 @@ if __name__ == "__main__":
         logging.info(f"Model loaded from {args.load}")
 
     net.to(device=device)
+
     try:
         train_net(
             net=net,
@@ -245,8 +256,6 @@ if __name__ == "__main__":
             batch_size=args.batch_size,
             learning_rate=args.lr,
             device=device,
-            img_scale=args.scale,
-            val_percent=args.val / 100,
             amp=args.amp,
         )
     except KeyboardInterrupt:
diff --git a/src/unet/model.py b/src/unet/model.py
index 73872c5..08d2807 100644
--- a/src/unet/model.py
+++ b/src/unet/model.py
@@ -20,7 +20,7 @@ class UNet(nn.Module):
         self.ups = nn.ModuleList()
         for i in range(len(features) - 1):
             self.ups.append(
-                Up(*features[-1 - i : -1 - i + 3 : -1]),
+                Up(*features[-1 - i : -3 - i : -1]),
             )
 
         self.outc = OutConv(features[0], n_classes)
diff --git a/src/utils/dataset.py b/src/utils/dataset.py
index 76e3a1f..6680bd8 100644
--- a/src/utils/dataset.py
+++ b/src/utils/dataset.py
@@ -3,6 +3,7 @@ from os import listdir
 from os.path import splitext
 from pathlib import Path
 
+import albumentations as A
 import numpy as np
 import torch
 from PIL import Image
@@ -10,16 +11,15 @@ from torch.utils.data import Dataset
 
 
 class SphereDataset(Dataset):
-    def __init__(self, images_dir: str, masks_dir: str, scale: float = 1.0, mask_suffix: str = ""):
+    def __init__(self, images_dir: str, transform: A.Compose, masks_dir: str = None):
         self.images_dir = Path(images_dir)
-        self.masks_dir = Path(masks_dir)
-        assert 0 < scale <= 1, "Scale must be between 0 and 1"
-        self.scale = scale
-        self.mask_suffix = mask_suffix
+        self.masks_dir = Path(masks_dir) if masks_dir else None
 
         self.ids = [splitext(file)[0] for file in listdir(images_dir) if not file.startswith(".")]
+
         if not self.ids:
             raise RuntimeError(f"No input file found in {images_dir}, make sure you put your images there")
+
         logging.info(f"Creating dataset with {len(self.ids)} examples")
 
     def __len__(self):
@@ -30,10 +30,7 @@ class SphereDataset(Dataset):
         w, h = pil_img.size
         newW, newH = int(scale * w), int(scale * h)
 
-        assert (
-            newW > 0 and newH > 0,
-            "Scale is too small, resized images would have no pixel",
-        )
+        assert newW > 0 and newH > 0, "Scale is too small, resized images would have no pixel"
 
         pil_img = pil_img.resize((newW, newH), resample=Image.NEAREST if is_mask else Image.BICUBIC)
         img_ndarray = np.asarray(pil_img)
@@ -61,25 +58,19 @@ class SphereDataset(Dataset):
 
     def __getitem__(self, idx):
         name = self.ids[idx]
+
         mask_file = list(self.masks_dir.glob(name + self.mask_suffix + ".*"))
         img_file = list(self.images_dir.glob(name + ".*"))
 
-        assert (
-            len(img_file) == 1,
-            f"Either no image or multiple images found for the ID {name}: {img_file}",
-        )
-        assert (
-            len(mask_file) == 1,
-            f"Either no mask or multiple masks found for the ID {name}: {mask_file}",
-        )
+        assert len(img_file) == 1, f"Either no image or multiple images found for the ID {name}: {img_file}"
+        assert len(mask_file) == 1, f"Either no mask or multiple masks found for the ID {name}: {mask_file}"
 
         mask = self.load(mask_file[0])
         img = self.load(img_file[0])
 
         assert (
-            img.size == mask.size,
-            f"Image and mask {name} should be the same size, but are {img.size} and {mask.size}",
-        )
+            img.size == mask.size
+        ), f"Image and mask {name} should be the same size, but are {img.size} and {mask.size}"
 
         img = self.preprocess(img, self.scale, is_mask=False)
         mask = self.preprocess(mask, self.scale, is_mask=True)
diff --git a/src/utils/paste.py b/src/utils/paste.py
new file mode 100644
index 0000000..0f87c7e
--- /dev/null
+++ b/src/utils/paste.py
@@ -0,0 +1,126 @@
+import os
+import random as rd
+
+import albumentations as A
+import cv2
+import numpy as np
+from PIL import Image
+
+
+class RandomPaste(A.DualTransform):
+    """Paste an object on a background.
+
+    Args:
+        TODO
+
+    Targets:
+        image, mask
+
+    Image types:
+        uint8
+    """
+
+    def __init__(
+        self,
+        nb,
+        scale_limit,
+        path_paste_img_dir,
+        path_paste_mask_dir,
+        always_apply=True,
+        p=1.0,
+    ):
+        super().__init__(always_apply, p)
+        self.path_paste_img_dir = path_paste_img_dir
+        self.path_paste_mask_dir = path_paste_mask_dir
+        self.scale_limit = scale_limit
+        self.nb = nb
+
+    @property
+    def targets_as_params(self):
+        return ["image"]
+
+    def apply(self, img, positions, paste_img, paste_mask, **params):
+        img = img.copy()
+
+        w, h = paste_mask.shape
+        mask_b = paste_mask > 0
+        mask_rgb_b = np.stack([mask_b, mask_b, mask_b], axis=2)
+
+        for (x, y) in positions:
+            img[x : x + w, y : y + h] = img[x : x + w, y : y + h] * ~mask_rgb_b + paste_img * mask_rgb_b
+
+        return img
+
+    def apply_to_mask(self, mask, positions, paste_mask, **params):
+        mask = mask.copy()
+
+        w, h = paste_mask.shape
+        mask_b = paste_mask > 0
+
+        for (x, y) in positions:
+            mask[x : x + w, y : y + h] = mask[x : x + w, y : y + h] * ~mask_b + mask_b
+
+        return mask
+
+    def get_params_dependent_on_targets(self, params):
+        filename = rd.choice(os.listdir(self.path_paste_img_dir))
+
+        paste_img = np.array(
+            Image.open(
+                os.path.join(
+                    self.path_paste_img_dir,
+                    filename,
+                )
+            ).convert("RGB"),
+            dtype=np.uint8,
+        )
+
+        paste_mask = (
+            np.array(
+                Image.open(
+                    os.path.join(
+                        self.path_paste_mask_dir,
+                        filename,
+                    )
+                ).convert("L"),
+                dtype=np.float32,
+            )
+            / 255
+        )
+
+        target_img = params["image"]
+
+        min_scale = min(
+            target_img.shape[0] / paste_img.shape[0],
+            target_img.shape[1] / paste_img.shape[1],
+        )
+
+        rescale_rotate = A.Compose(
+            [
+                A.Rotate(limit=360, always_apply=True, border_mode=cv2.BORDER_CONSTANT),
+                A.RandomScale(scale_limit=(min_scale * self.scale_limit - 1, -0.99), always_apply=True),
+            ],
+        )
+
+        augmentations = rescale_rotate(image=paste_img, mask=paste_mask)
+        paste_img = augmentations["image"]
+        paste_mask = augmentations["mask"]
+
+        positions = []
+        for _ in range(rd.randint(1, self.nb)):
+            x = rd.randint(0, target_img.shape[0] - paste_img.shape[0])
+            y = rd.randint(0, target_img.shape[1] - paste_img.shape[1])
+            positions.append((x, y))
+
+        params.update(
+            {
+                "positions": positions,
+                "paste_img": paste_img,
+                "paste_mask": paste_mask,
+            }
+        )
+
+        return params
+
+    def get_transform_init_args_names(self):
+        return "scale_limit", "path_paste_img_dir", "path_paste_mask_dir"