diff --git a/docs/doc/ml/models.md b/docs/doc/ml/models.md
index 2b50e6400260dca83d9a640e2a67d60add23e4ab..e014072d20dff6f801cf164545d55e37cdcccb19 100644
--- a/docs/doc/ml/models.md
+++ b/docs/doc/ml/models.md
@@ -1,7 +1,6 @@
 ---
 hide:
   - navigation
-  - toc
 ---
 
 # Machine learning models
@@ -16,4 +15,10 @@ The `qim3d` library aims to ease the creation of ML models for volumetric images
 ::: qim3d.ml.models
     options:
         members:
-            - UNet
\ No newline at end of file
+            - UNet
+
+::: qim3d.ml
+    options: 
+        members:
+            - prepare_datasets
+            - prepare_dataloaders
diff --git a/docs/notebooks/UNet.ipynb b/docs/notebooks/UNet.ipynb
index 0b11555bc8fd8e75302704a43b1fd6f728a1a7f0..25e0694859bea31b7943da81897c9b067b1a0434 100644
--- a/docs/notebooks/UNet.ipynb
+++ b/docs/notebooks/UNet.ipynb
@@ -576,19 +576,8 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 1000x200 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x200 with 5 Axes>"
+       "<All keys matched successfully>"
       ]
      },
      "execution_count": 1,
@@ -647,14 +636,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of volumes: 2\n",
+      "Number of train volumes: 2\n",
       "Volume shape: (128, 128, 128)\n",
       "Target shape: (128, 128, 128)\n",
       "Preds shape: (128, 128, 128)\n"
@@ -662,7 +651,7 @@
     },
     {
      "data": {
-      "image/png": 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qrzOwNWP2AAAAkBhj9pCDMbDqqUrHoh4dLfkgS54clb2rmmI+Utn+FfW9k8rfb7TsP6CxjNkDDENVDsCgWQYe7MtX49XrOucUXreiFvIRaV6qAKTJmD0AAAAkRmceqDwdFGi8IndSSUcK76Mq7FNSeB2gCnTmgUqrwkEXUH5V2JYpIAEGU8wDAABAYozZA5VUhS4WQKp04YvJ6wLFopgHKkch/yYHZVAtm3vXFTb3RV0u/strBMVjzB4AAAASozMPAJC4Rn22eV83tpHPTXF5jaDYFPNAZRivf5ODMyingdku8vaurNugIv/Nh6Osrw+UkTF7AAAASExbrVar5XpgmyY+1VWr9W73+/KRhrJ0TUajER0X+YBsRchH37ZvtPkfzTa07N3eMuxfWvEaFSEfUFRD5SNCMQ+52NmUQxkOtkai0Qdo8gHZyp6PrO1q2Yv3PmXZr7Tq9Sp7PmA08hTzxuwBAAAgMU53AZRUVTpjQOvYzpTD5t51XktIkM48UBmdHV1NO1hp5u/K+v0ANE5ZRuyBdCnmAQAAIDFugAc5uEFL+TSiozKcbnirf389yQdkk4/yKmtnvpn7EvmAbO5mD3ViZ1NeRfiopdEeELZ6pF4+IJt8lFcri/ms7X69lqlZ+xX5gGzuZg8AAAAlpDMPOThzTDPl6ay0uhs/kHxANvkot1Z05/Nu/+uxbI3e18gHZDNmD3ViZwPZ5AOyyUe5tWrUPk+RncLIvXxANmP2AAAAUEI685CDM8eQTT4gm3xUR5FG7uu5LDrz0BrG7KFO7Gwgm3xANvmoHh9Zl598QDZj9gAAAFBCTncBufV1G956dn6oLkRnR9egn816HgBI3cB9W1m79EAxGLOHHIyBNeeApO8AaHPvOoV+QuQDsslHtZWpmDdmD81lzB4AAABKSGcecqjameOidRJ06YutavmA4ZAPirZPHSmdeWiuPJ15CQEiotgHG66xBwCAwYzZAwAAQGJ05oFCd+UHGricuvQAAFSZa+Yhh7Je05VKEZ9FQV8MZc0H1IN80Mc+d2vyAdnczR4AAABKyOkuqJjUOwMAkKLOji77YKCuFPNQcmU+cHCXewAAqsqYPQAAACTGDfAghxRv0FLmjvy26M63Tor5gGaRD7YlpX10I/ev8gHZ8twATzEPOaSws0npwKDeFPKtlUI+oFXkg6EUef/d6P2rfEA2d7MHAACAEnK6CxJW5LP5AMDQRnuX+zzd8+E+v4k3SINiHgAAWmhg8Zyn8B5usa04h3IyZg8AAACJ0ZmHBBmvB4D88u43i9DBLsIyAGlQzAMAUErDPfnd93gFNZACY/YAAACQGMU8JMaIPQAMbTT7S/taIAXG7CERDiy2ZgwSgIHsK4Eq0ZkHAACAxCjmgSTpygOUx8CO+ubedf3/tvW4ZnXfm/m7AEbCmD0kwMEEAGUxcJ828MRsVvE+3OcEqAqdeQAAAEhMW61Wq+V6YJsmPtVVq/Vu9/vNyIeuw5uM1xdPEfIBRSUfW6vX/myorn492fc0hnxAtqHyEWHMHgCABqt3sf3W4rrvaye+gSoxZg8AAACJ0ZkHCs94IwAADKYzDxSaQh4gbc0cfW/UPsPH1AFFpJgHAACAxBizh4oZeJOgoToYAx/T7M6KDghAuhq1Dc/TeXczPKAqdOYhEZ0dXXUdHxzOAVHex9dTvdcXAADKRDEPAAAAiTFmDxVQrw531vPUY5RRFx4gfUUabXfJFlB2bbVarZbrgW3qfqqrVuvd7vebmY/hHpi0skjOWlaFe7kUKR9QNFXKRzMK55HuP+q5bPZh9VOlfMBwDZWPCGP2AAAAkBydecihaGeO83QYdA5olqLlA4qkSvkocmc+ov7LZz87elXKBwxXns68hECCBh5AbOvgxAEGAAy2rX2ja+qBlBmzBwAAgMTozEPidOEBKLNG7ufc8R5Imc48AACjlurJ5c6Orv5/AClRzAMAAEBi3M0ecnC3VcgmH5Ctqvmo1+h6s7vlI11uXf2RqWo+II88d7NXzEMOdjaQTT4gm3wMv0AuQmGc4jKnSD4gW55i3pg9AAAAJEYxDwBAw+haAzSG2RUAABpKQQ9QfzrzAAAAkBjFPAAAACTGmD0AAAyTSweAVtOZBwAAgMTozAMAQAz/8+UBWklnHgCAylPIA6lRzAMAAEBijNkDAFBJuvFAyhTzAACU0sBive/u8wp4oCyM2QMAAEBiFPMAALRUM7rlm3vX6coDpWLMHgCAptheMb2t7/WNxtfj+QHKRmceAAAAEqMzDwBAQ420Y/7Wnxtupx6gzBTzAADUVaPG3Y3RA/yXMXsAAABIjM48AAB1UYXOuVF/oCgU8wAAjFgVCvg+CnmgSIzZAwAAQGJ05gEAGJYqdeMjdOSBYtKZBwCADAp5oKgU8wAAAJAYY/YAAORWhRF73XggBW21Wq2W64Ft6n6qq1br3e735YMqkw/IVtZ8lLWgV8Q3V1nzAfUwVD4ijNkDAABAchTzAABUnq48kBqzKwAAVJYiHkiVzjwAAAAkxg3wIAc3aIFs8gHZyp6P1G+EpyvfWmXPB4yGG+ABAABACSnmAQAAIDHG7CEHY2CQTT4gWxXykeKovfH6YqhCPmCk8ozZK+YhBzsbyCYfkK1K+Sh6Ua+AL54q5QOGyzXzAAAAUEKKeQAARk3nG6C5zK4AAFAX9Sjoiz6uD1AUOvMAAACQGMU8AACF0YhxfZcAAGXkbvaQg7utQjb5gGzyMXqjGbtXxBebfEA2d7MHAACAEtKZhxycOYZs8gHZ5AOyyQdk05kHAACAElLMAwAAQGIU8wAAAJAYxTwAAAAkxl0lKLxDD/2fVi8CFJZ8QDb5gGzyAdlSyYfOPKUzbdq0mDZtWu7Hz5w5M37xi1/EzTffHFOnTm3gkkHryQdkkw/IJh+QrVX50JmndFauXJn7sQcccEBMnTo1TjrppGhvb49rr702NmzYEE8//XTjFhBaSD4gm3xANvmAbK3Kh848hTdmzJg4//zzY9myZXHLLbfE4sWLY+zYsXHsscfGmjVrYscdd4w999wz1q5dG5MmTYrZs2fH7Nmzo62tLebMmRPd3d1xww03xOzZs7d67mOPPTbWrVsXPT098eqrr8a6devi+OOPb8FawsjIB2STD8gmH5AtlXwo5im8Qw45JLZs2RKzZs2KE088MTo7O+OYY46JO++8Mx544IE4/fTT48ILL4yf/OQn8eyzz/b/3OTJk+Pggw+OGTNmxKmnnhqTJk2KsWPHDnru3XffPTZt2tT/9aZNm2L33Xdv2rrBaMkHZJMPyCYfkC2VfBizp/A2bNgQ//znP2P69Omx3377xb777hs77bRTRERcdtllsXLlynjiiSdi1apVg37u2WefjR122CGuueaaWL9+fVx55ZXR09Mz6DFtbW1b/b5arda4lYE6kw/IJh+QTT4gWyr50Jmn8I477rj43ve+Fz09PXHzzTfHhg0b+r+36667Rq1Wi3322SfGjRs36Od6enpi5syZ8dOf/jQmTJgQy5Yti0mTJg16zAsvvBATJ07s/3rixImxcePGxq4Q1JF8QDb5gGzyAdlSyYdinsI76qijYu3atbFmzZp48cUX4wMf+ECMGTMm2traYv78+bFo0aK4/fbb4+yzzx70c4ceemhceeWVcd9998Xll18eTz75ZLzrXe8a9Jj169dHV1dXjBs3LsaNGxddXV2xfv36Jq4djI58QDb5gGzyAdlSyYcxewpv1apVsWDBgpgyZUr85z//iT/96U+x9957xymnnBIvvfRSrF27Nu6444646aab4uijj+7/uQcffDCefvrpuOmmm+L111+PRx55JO6+++5Bz/3www/H6tWr47rrrouOjo5YuXJlPPLII81eRRgx+YBs8gHZ5AOypZKPtlrOAf22NnU/rXHoof/T6kWIBx54YLvflw9aRT4gm3xANvmAbCnkI2IYxTwAAABQDK6ZBwAAgMQo5gEAACAxinkAAABIjGIeAAAAEqOYBwAAgMQo5gEAACAxinkAAABIjGIeAAAAEqOYBwAAgMT8H0ZsSjjYq6qzAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 1000x200 with 5 Axes>"
       ]
@@ -672,12 +661,12 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x200 with 5 Axes>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -698,7 +687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -713,7 +702,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x200 with 5 Axes>"
       ]
@@ -723,12 +712,12 @@
     },
     {
      "data": {
-      "image/png": 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ne5qVgVaPccteukZ7c7yyfCwEqKYkPjNf747WwZhmG+1nupwkNF8r893J9yvl/ZbPPFKvZtyVvJl5bWX+qpSPoh3z8ryvPjPfWVXKB9TLZ+YBAACghFzuApJR1m5HWdeLchuuo5l3W27GNj+2Z0pTuqryV15FmxQAaDbFPFB4TrahWLKKpGaMz9dj4DlGU7TZr9Bstimg3YzZAwAAQGJKeQO8Aa6Q0iz13qDFaF/ztDvH7XzvyrKPcgOj6inyjWnzLFs7l6dK+ajysa8s+/N2q1I+oF55boCXRELqHaOzQ4X0yTGUR7vH74djn9J6zbqPQUpsV0AnGbMHAACAxCTRmYeq69R35XZKFTodVVhHyqcZ+5lWdOnzLNem/rVyR1PZnoBOU8xDCzRyh+Xh/k7ex6Ze0BfhxKhZ7x2UkXwwkipsI0U4VgFEGLMHAACA5CRxN/vhvPWKr6uktFIjd1tt9bcxlKX7UbQMt+p1Ldp6NoO7EVdHK3Ix2kw0sizuZt8eZTk+bamM+/BOqnI+YCSluZv9cOxMKauqbdtVWt8qrSvlVIURahiO/TdQRMbsAQAAIDHJduYhFVtezR+pm+Wu9cXRzBsKFnk9ITVl2geSBvtwoKgU89BGWSOqivhiamSkOJV1BGiV1D+WYT8OFJ0xewAAAEhMsnezh3bq1N1WU+1mbEsZOh3DvS9lWK/RcjfiaurEx1CauU9sV2blI91jWZX36+0iH5Ct1HezB9JSppOiMq0LjFbqI9QAkDpj9gAAAJAYnXkoqJS6XTrVUF2NfPND3n1Hs/eH9lnFVu/704rjpW0ESIFiHhgVJzrAgOH2B5v61w4ZxR/tPiOlC5s0ZrTbSDO/ShQgJcbsAQAAIDE681AwRe4u6MYDeW25v7DvqKZ6jmeNbiNuyAhUkWIeCqaoJyROxoHU2Y+1V1GPZ9tiGwFSYsweAAAAEqMzD2TSoQA6rR13y6c83AwPqJKuWq1Wy/XALnU/1VWr9W/z563IRydPRpwAU49O5ANSIR9vyjqmtfJ4M5rjqONfe8kHZBspHxHG7AEAACA5OvOQQ6euHLe6O68DQTPorEA2+eisdt5Rn/rJB2TL05lXzEMOnT7YtKKod9LSOtt6v8r4unc6H1Bk8lEMnRjzZ2TyAdmM2QMAAEAJ6cxDDkW4ctyM7rwOxOj4uMO2FSEfUFTyAdnkA7IZs4cmKfLBZstCM/WisEja/W0CKb93Rc4HdJp8QDb5gGzG7AEAAKCEdOYhB1eOq6PdHfnhpNallw/IJh+QTT4gm848QII29a8txEUFAACKSzEPAAAAiTFmDzkYAyu3FLrgRR69lw/IJh+QTT4gmzF7gBzG9kwpdLEMAABvpZgHAACAxBizhxyMgZVbCmP2WyraFIF8QDb5gGzyAdmM2QOUUGoXHwAAaD7FPAAAACRGMQ+QIN9FDwBQbT6IApCwLQv6on2WHgCA1tGZBwAAgMTozAOUhC49AEB1+Go6yMFXp1RD2T6D3q6CXj4gm3xke+s+d2Cftal/rQuSFSEfkM1X0wEAAEAJ6cxDDq4cp2WkDvu2Oj5l684PaGWXSz4gm3wMVe8+Voe+3OQDsuXpzCvmIQcHm3TkPVGs5wSxTAV+K06M5QOyycdQinm2JB+QzZg9AAAAlJDOPOTgynHxjbZ73mjXJ+WufbM6XvKRX6e2UzpHPt5k22c48gHZ8nTmJQQohS3vgtyJ591SygU+zdeM7SHrrt8AQHUZswcAAIDEGLOHHIyBFV8KI5xF7Ng3Y/3lY2udeK9164tJPoZyAzy2JB+Qzd3soUkcbIqvkeKpqCeL7SwIG3kN5GOoTl60Keq2XGXykc+m/rW23wqSD8jmbvYAAABQQi53ARTU2J4phRzNZ2tFeZ8GlkOHk9TYZgHqp5gHgFEqShH/VlsulyIJAMrJmD0AAAAkRjEPQGE7zEXmNQMAOkkxD1BgY3umGJMumE39a5Mq5FNaVgAgP8U8AAAAJEYxD5AA3fliSLXLndo0AQAwMsU8UArG0QEAqBLFPAAAACRGMQ+Uiu48rWBMHQAoGsU8UGkpFf8+StAZZSriy7QuAFB1inkAAABITE+nFwCgE3S4h/J6AACkRTEPlI7CtH6b+td63d7CSDoAUGTG7AEAACAxinkAdOUBABKjmAeAtzBiDwAUnWIeAAAAEqOYBwAAgMS4mz1AnQZGsMvwOfMyrEMzGa8HAFKhMw8AAACJ0ZkHyDBSlzbr563qdusa0yiTGABQHop5gGE0Ujhv+bvNKp4U8q1X5tdYEQ8A5WPMHgAAABKjMw/w/7WiM5t3FL/MXeGiK/trrysPAOWkmAfogLIXkAAAtJYxewAAAEiMzjxQebrk1VXm9954PQCUm848UHlje6YofCgV2zMAlJ9iHgAAABJjzB6ovDKPWo9kYN11ctPnPQSAalHMA5U3UARVuagnXYp4AKgmY/YAAACQGJ15ACprbM+U5CYydOIBgAjFPAAVl8rHLBTxAMCWjNkDAABAYnTmASC27nx3ulOvEw8AbItiHgCGMVwx3aoCX+EOANTLmD0AAAAkRmceAHLaVgd9pK697jsA0ExdtVqtluuBXep+qqtW69/mz+WjPDr9OelOaaTQlA/IJh+QTT4g20j5iDBmDwAAAMlRzAMAAEBijNlDDsbAqqcq4/bN+By3fEA2+YBs8gHZjNkDAABACSnmAYZRhTuPV2EdAQDKypg95GAMrNraMXI/UFi387maRT4gm3xANvmAbMbsAQAAoIR05iEHV45pdsd8NN3xZixDK0br5QOyyQdkkw/Ilqczr5iHHBxs2FK9RXWrP5uetTzt+ky8fEA2+YBs8gHZjNkDAABACenMQw6uHDOSLbvjVbtLvHxANvmAbPIB2YzZQ5M42EA2+YBs8gHZ5AOyGbMHAACAElLMAwAAQGIU8wAAAJAYxTwAAAAkRjEPAAAAiVHMAwAAQGIU8wAAAJAYxTwAAAAkRjEPAAAAienp9ALASA455H87vQhQWPIB2eQDsskHZEslHzrzlM60adNi2rRpuR8/c+bM+MUvfhE333xzTJ06tYVLBp0nH5BNPiCbfEC2TuVDZ57SWbFiRe7H7r///jF16tQ46aSToru7O6699tpYv359PP30061bQOgg+YBs8gHZ5AOydSofOvMU3pgxY+L888+PpUuXxi233BKLFy+OcePGxTHHHBOrV6+O7bffPvbYY49Ys2ZNTJo0KWbPnh2zZ8+Orq6umDNnTvT29sYNN9wQs2fP3upvH3PMMbF27dro6+uLV199NdauXRvHH398B9YSRkc+IJt8QDb5gGyp5EMxT+EdfPDBsXnz5pg1a1aceOKJMXbs2Dj66KPjzjvvjAceeCBOP/30uPDCC+MnP/lJPPvss4O/N3ny5DjooINixowZceqpp8akSZNi3LhxQ/72brvtFhs3bhz898aNG2O33XZr27pBo+QDsskHZJMPyJZKPozZU3jr16+Pf/7znzF9+vTYd999Y5999okddtghIiJ+8IMfxIoVK+KJJ56IlStXDvm9Z599Nrbbbru45pprYt26dXHFFVdEX1/fkMd0dXVt9Xy1Wq11KwNNJh+QTT4gm3xAtlTyoTNP4R177LHx3e9+N/r6+uLmm2+O9evXD/5sl112iVqtFnvvvXeMHz9+yO/19fXFzJkz46c//WnstNNOsXTp0pg0adKQx7zwwgsxceLEwX9PnDgxNmzY0NoVgiaSD8gmH5BNPiBbKvlQzFN4Rx55ZKxZsyZWr14dL774Yrz//e+PMWPGRFdXV8yfPz8WLVoUt99+e5x99tlDfu+QQw6JK664Iu6777647LLL4sknn4x3vvOdQx6zbt26mDJlSowfPz7Gjx8fU6ZMiXXr1rVx7aAx8gHZ5AOyyQdkSyUfxuwpvJUrV8aCBQviuOOOi//85z/xpz/9Kfbaa6845ZRT4qWXXoo1a9bEHXfcETfddFMcddRRg7/34IMPxtNPPx033XRTvP766/HII4/E3XffPeRvP/zww7Fq1aq47rrroqenJ1asWBGPPPJIu1cRRk0+IJt8QDb5gGyp5KOrlnNAv6tL3U9nHHLI/3Z6EeKBBx7Y5s/lg06RD8gmH5BNPiBbCvmIqKOYBwAAAIrBZ+YBAAAgMYp5AAAASIxiHgAAABKjmAcAAIDEKOYBAAAgMYp5AAAASIxiHgAAABKjmAcAAIDEKOYBAAAgMf8Pr1XJMJAMebgAAAAASUVORK5CYII=",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x200 with 5 Axes>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/qim3d/ml/_data.py b/qim3d/ml/_data.py
index 0fe9840f864fbaa66b844436a3ae9c1bbc3e80f6..72f8f6db7d8259d5eaa9611c34428942d8b465d6 100644
--- a/qim3d/ml/_data.py
+++ b/qim3d/ml/_data.py
@@ -1,12 +1,11 @@
 """Provides a custom Dataset class for building a PyTorch dataset."""
+
 from pathlib import Path
-from PIL import Image
 from qim3d.utils import log
 import torch
 import numpy as np
 import nibabel as nib
 from typing import Optional, Callable
-import torch.nn as nn
 from ._augmentations import Augmentation
 
 class Dataset(torch.utils.data.Dataset):
@@ -33,11 +32,6 @@ class Dataset(torch.utils.data.Dataset):
     Methods:
         __len__(): Returns the total number of samples in the dataset.
         __getitem__(idx): Returns the image and its target segmentation at the given index.
-
-    Usage:
-        dataset = Dataset(root_path="path/to/dataset", split="train", 
-            transform=albumentations.Compose([ToTensorV2()]))
-        image, target = dataset[idx]
     """
     def __init__(self, root_path: str, split: str = "train", transform: Optional[Callable] = None):
         super().__init__()
@@ -169,7 +163,7 @@ def check_resize(
 def prepare_datasets(
         path: str, 
         val_fraction: float, 
-        model: nn.Module, 
+        model: torch.nn.Module, 
         augmentation: Augmentation,
         ) -> tuple[torch.utils.data.Subset, torch.utils.data.Subset, torch.utils.data.Subset]:
     """
@@ -177,12 +171,28 @@ def prepare_datasets(
 
     Args:
         path (str): Path to the dataset.
-        val_fraction (float): Fraction of the data for the validation set.
+        val_fraction (float): Fraction of the data for the validation set. 
         model (torch.nn.Module): PyTorch Model.
-        augmentation (albumentations.core.composition.Compose): Augmentation class for the dataset with predefined augmentation levels.
+        augmentation (monai.transforms.Compose): Augmentation class for the dataset with predefined augmentation levels.
 
     Raises:
-        ValueError: if the validation fraction is not a float, and is not between 0 and 1. 
+        ValueError: If the validation fraction is not a float, and is not between 0 and 1. 
+    
+    Example:
+        ```python
+        import qim3d
+
+        base_path = "C:/dataset/"
+        model = qim3d.ml.models.UNet(size = 'small')
+        augmentation =  qim3d.ml.Augmentation(resize = 'crop', transform_train = 'light')
+
+        train_set, val_set, test_set = qim3d.ml.prepare_datasets(
+            path = base_path,
+            val_fraction = 0.5,
+            model = model,
+            augmentation = augmentation
+            )
+        ```
     """
     
     if not isinstance(val_fraction,float) or not (0 <= val_fraction < 1):
@@ -226,12 +236,35 @@ def prepare_dataloaders(train_set: torch.utils.data,
 
     Args:
         train_set (torch.utils.data): Training dataset. 
-        val_set (torch.utils.data):   Validation dataset.
-        test_set (torch.utils.data):  Testing dataset.
+        val_set (torch.utils.data): Validation dataset.
+        test_set (torch.utils.data): Testing dataset.
         batch_size (int): Size of the batches that should be trained upon.
         shuffle_train (bool, optional): Optional input to shuffle the training data (training robustness).
-        num_workers (int, optional): Defines how many processes should be run in parallel.
-        pin_memory (bool, optional): Loads the datasets as CUDA tensors.
+        num_workers (int, optional): Defines how many processes should be run in parallel. Default is 8.
+        pin_memory (bool, optional): Loads the datasets as CUDA tensors. Default is False.
+    
+    Example:
+        ```python
+        import qim3d
+
+        base_path = "C:/dataset/"
+        model = qim3d.ml.models.UNet(size = 'small')
+        augmentation =  qim3d.ml.Augmentation(resize = 'crop', transform_train = 'light')
+
+        train_set, val_set, test_set = qim3d.ml.prepare_datasets(
+            path = base_path,
+            val_fraction = 0.5,
+            model = model,
+            augmentation = augmentation
+            )
+
+        train_loader, val_loader, test_loader = qim3d.ml.prepare_dataloaders(
+            train_set = train_set, 
+            val_set = val_set,
+            test_set = test_set,
+            batch_size = 1,
+            )
+        ```
     """
     from torch.utils.data import DataLoader
 
@@ -239,4 +272,4 @@ def prepare_dataloaders(train_set: torch.utils.data,
     val_loader = DataLoader(dataset=val_set, batch_size=batch_size, num_workers=num_workers, pin_memory=pin_memory)
     test_loader = DataLoader(dataset=test_set, batch_size=batch_size, num_workers=num_workers, pin_memory=pin_memory)
 
-    return train_loader,val_loader,test_loader
\ No newline at end of file
+    return train_loader, val_loader, test_loader
\ No newline at end of file
diff --git a/qim3d/ml/models/_unet.py b/qim3d/ml/models/_unet.py
index b2950a2b21e939aaf35f038347f0e438e05a7a5b..d5f49508417e8e901032af5a662cb3fe663e200b 100644
--- a/qim3d/ml/models/_unet.py
+++ b/qim3d/ml/models/_unet.py
@@ -6,9 +6,7 @@ from qim3d.utils import log
 
 class UNet(nn.Module):
     """
-    3D UNet model for QIM imaging.
-
-    This class represents a 3D UNet model designed for imaging segmentation tasks.
+    3D UNet model designed for imaging segmentation tasks.
 
     Args:
         size ('small' or 'medium' or 'large', optional): Size of the UNet model. Must be one of 'small', 'medium', or 'large'. Defaults to 'medium'.
@@ -21,6 +19,13 @@ class UNet(nn.Module):
 
     Raises:
         ValueError: If `size` is not one of 'small', 'medium', or 'large'.
+    
+    Example:
+        ```python
+        import qim3d
+
+        model = qim3d.ml.models.UNet(size = 'small')
+        ```
     """
 
     def __init__(