diff --git a/docs/save_load.rst b/docs/save_load.rst
index e225c5d0..e90e4eba 100644
--- a/docs/save_load.rst
+++ b/docs/save_load.rst
@@ -59,6 +59,37 @@ For example:
     # Or saved and pushed to the Hub simultaneously
     model.save_pretrained('username/my-model', push_to_hub=True, metrics={'accuracy': 0.95}, dataset='my_dataset')
 
+Saving with preprocessing transform (Albumentations)
+----------------------------------------------------
+
+You can save the preprocessing transform along with the model and push it to the Hub. 
+This can be useful when you want to share the model with the preprocessing transform that was used during training, 
+to make sure that the inference pipeline is consistent with the training pipeline.
+
+.. code:: python
+
+    import albumentations as A
+    import segmentation_models_pytorch as smp
+
+    # Define a preprocessing transform for image that would be used during inference
+    preprocessing_transform = A.Compose([
+        A.Resize(256, 256),
+        A.Normalize()
+    ])
+
+    model = smp.Unet()
+
+    directory_or_repo_on_the_hub = "qubvel-hf/unet-with-transform"  # <username>/<repo-name>
+
+    # Save the model and transform (and pus ot hub, if needed)
+    model.save_pretrained(directory_or_repo_on_the_hub, push_to_hub=True)
+    preprocessing_transform.save_pretrained(directory_or_repo_on_the_hub, push_to_hub=True)
+
+    # Loading transform and model
+    restored_model = smp.from_pretrained(directory_or_repo_on_the_hub)
+    restored_transform = A.Compose.from_pretrained(directory_or_repo_on_the_hub)
+
+    print(restored_transform)
 
 Conclusion
 ----------
@@ -71,4 +102,6 @@ By following these steps, you can easily save, share, and load your models, faci
     :target: https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/binary_segmentation_intro.ipynb
     :alt: Open In Colab
 
-
+.. |colab-badge| image:: https://colab.research.google.com/assets/colab-badge.svg
+    :target: https://colab.research.google.com/github/qubvel/segmentation_models.pytorch/blob/main/examples/save_load_model_and_share_with_hf_hub.ipynb
+    :alt: Open In Colab
diff --git a/examples/save_load_model_and_share_with_hf_hub.ipynb b/examples/save_load_model_and_share_with_hf_hub.ipynb
index d6d4c0f4..d27a5a0a 100644
--- a/examples/save_load_model_and_share_with_hf_hub.ipynb
+++ b/examples/save_load_model_and_share_with_hf_hub.ipynb
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -82,9 +82,11 @@
       "license: mit\n",
       "pipeline_tag: image-segmentation\n",
       "tags:\n",
+      "- model_hub_mixin\n",
+      "- pytorch_model_hub_mixin\n",
+      "- segmentation-models-pytorch\n",
       "- semantic-segmentation\n",
       "- pytorch\n",
-      "- segmentation-models-pytorch\n",
       "languages:\n",
       "- python\n",
       "---\n",
@@ -157,7 +159,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "075ae026811542bdb4030e53b943efc7",
+       "model_id": "1d6fe9d868c24175aa5f23a2893a2c21",
        "version_major": 2,
        "version_minor": 0
       },
@@ -179,13 +181,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2921a81d7fd747939b4a425cc17d6104",
+       "model_id": "2f4f5e4973e44f9a857e89d9ac707b53",
        "version_major": 2,
        "version_minor": 0
       },
@@ -199,10 +201,10 @@
     {
      "data": {
       "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/qubvel-hf/unet-with-metadata/commit/9f821c7bc3a12db827c0da96a31f354ec6ba5253', commit_message='Push model using huggingface_hub.', commit_description='', oid='9f821c7bc3a12db827c0da96a31f354ec6ba5253', pr_url=None, pr_revision=None, pr_num=None)"
+       "CommitInfo(commit_url='https://huggingface.co/qubvel-hf/unet-with-metadata/commit/4ac3d2925d34cf183dc79a2e21b6e2f4bfe87586', commit_message='Push model using huggingface_hub.', commit_description='', oid='4ac3d2925d34cf183dc79a2e21b6e2f4bfe87586', pr_url=None, pr_revision=None, pr_num=None)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -224,6 +226,158 @@
     "\n",
     "# see result here https://huggingface.co/qubvel-hf/unet-with-metadata"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save model with preprocessing (using albumentations)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -U albumentations numpy==1.*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import albumentations as A\n",
+    "import segmentation_models_pytorch as smp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# define a preprocessing transform for image that would be used during inference\n",
+    "preprocessing_transform = A.Compose([\n",
+    "    A.Resize(256, 256),\n",
+    "    A.Normalize()\n",
+    "])\n",
+    "\n",
+    "model = smp.Unet()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1aa3f4db4cd2489baeac3b844977d5a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/97.8M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "CommitInfo(commit_url='https://huggingface.co/qubvel-hf/unet-with-transform/commit/680dad16431fa6efbb25832d33a24056bdf7dc1a', commit_message='Push transform using huggingface_hub.', commit_description='', oid='680dad16431fa6efbb25832d33a24056bdf7dc1a', pr_url=None, pr_revision=None, pr_num=None)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "directory_or_repo_on_the_hub = \"qubvel-hf/unet-with-transform\"\n",
+    "\n",
+    "# save the model\n",
+    "model.save_pretrained(directory_or_repo_on_the_hub, push_to_hub=True)\n",
+    "\n",
+    "# save transform\n",
+    "preprocessing_transform.save_pretrained(directory_or_repo_on_the_hub, push_to_hub=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's restore model and preprocessing transform for inference:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading weights from local directory\n",
+      "Compose([\n",
+      "  Resize(p=1.0, height=256, width=256, interpolation=1),\n",
+      "  Normalize(p=1.0, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=255.0, normalization='standard'),\n",
+      "], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}, is_check_shapes=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "restored_model = smp.from_pretrained(directory_or_repo_on_the_hub)\n",
+    "restored_transform = A.Compose.from_pretrained(directory_or_repo_on_the_hub)\n",
+    "\n",
+    "print(restored_transform)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Compose([\n",
+      "  HorizontalFlip(p=0.5),\n",
+      "  RandomBrightnessContrast(p=0.2, brightness_limit=(-0.2, 0.2), contrast_limit=(-0.2, 0.2), brightness_by_max=True),\n",
+      "  ShiftScaleRotate(p=0.5, shift_limit_x=(-0.0625, 0.0625), shift_limit_y=(-0.0625, 0.0625), scale_limit=(-0.09999999999999998, 0.10000000000000009), rotate_limit=(-45, 45), interpolation=1, border_mode=4, value=0.0, mask_value=0.0, rotate_method='largest_box'),\n",
+      "], p=1.0, bbox_params=None, keypoint_params=None, additional_targets={}, is_check_shapes=True)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# You can also save training augmentations to the Hub too (and load it back)!\n",
+    "#! Just make sure to provide key=\"train\" when saving and loading the augmentations.\n",
+    "\n",
+    "train_augmentations = A.Compose([\n",
+    "    A.HorizontalFlip(p=0.5),\n",
+    "    A.RandomBrightnessContrast(p=0.2),\n",
+    "    A.ShiftScaleRotate(p=0.5),\n",
+    "])\n",
+    "\n",
+    "train_augmentations.save_pretrained(directory_or_repo_on_the_hub, key=\"train\", push_to_hub=True)\n",
+    "\n",
+    "restored_train_augmentations = A.Compose.from_pretrained(directory_or_repo_on_the_hub, key=\"train\")\n",
+    "print(restored_train_augmentations)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See saved model and `albumentations` configs on the hub: https://huggingface.co/qubvel-hf/unet-with-transform/tree/main"
+   ]
   }
  ],
  "metadata": {