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@@ -69,12 +69,14 @@ Congratulations! You are done! Now you can train your model with your favorite f
 ### 📦 Models <a name="models"></a>
 
 #### Architectures <a name="architectires"></a>
- - [Unet](https://arxiv.org/abs/1505.04597) and [Unet++](https://arxiv.org/pdf/1807.10165.pdf)
- - [Linknet](https://arxiv.org/abs/1707.03718)
- - [FPN](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf)
- - [PSPNet](https://arxiv.org/abs/1612.01105)
- - [PAN](https://arxiv.org/abs/1805.10180)
- - [DeepLabV3](https://arxiv.org/abs/1706.05587) and [DeepLabV3+](https://arxiv.org/abs/1802.02611)
+ - Unet [[paper](https://arxiv.org/abs/1505.04597)] [[docs](https://smp.readthedocs.io/en/latest/models.html#unet)]
+ - Unet++ [[paper](https://arxiv.org/pdf/1807.10165.pdf)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id2)]
+ - Linknet [[paper](https://arxiv.org/abs/1707.03718)] [[docs](https://smp.readthedocs.io/en/latest/models.html#linknet)]
+ - FPN [[paper](http://presentations.cocodataset.org/COCO17-Stuff-FAIR.pdf)] [[docs](https://smp.readthedocs.io/en/latest/models.html#fpn)]
+ - PSPNet [[paper](https://arxiv.org/abs/1612.01105)] [[docs](https://smp.readthedocs.io/en/latest/models.html#pspnet)]
+ - PAN [[paper](https://arxiv.org/abs/1805.10180)] [[docs](https://smp.readthedocs.io/en/latest/models.html#pan)]
+ - DeepLabV3 [[paper](https://arxiv.org/abs/1706.05587)] [[docs](https://smp.readthedocs.io/en/latest/models.html#deeplabv3)]
+ - DeepLabV3] [[paper](https://arxiv.org/abs/1802.02611)] [[docs](https://smp.readthedocs.io/en/latest/models.html#id8)]
 
 #### Encoders <a name="encoders"></a>
 
 
@@ -11,6 +11,7 @@ Welcome to Segmentation Models's documentation!
    :caption: Contents:
 
    install
+   quickstart
    models
    encoders
 
 
@@ -0,0 +1,31 @@
+⏳ Quick Start
+==============
+
+Segmentation model is just a PyTorch nn.Module, which can be created as easy as:
+
+.. code-block:: python
+    
+    import segmentation_models_pytorch as smp
+
+    model = smp.Unet(
+        encoder_name="resnet34",        # choose encoder, e.g. mobilenet_v2 or efficientnet-b7
+        encoder_weights="imagenet",     # use `imagenet` pretreined weights for encoder initialization
+        in_channels=1,                  # model input channels (1 for grayscale images, 3 for RGB, etc.)
+        classes=3,                      # model output channels (number of classes in your dataset)
+    )
+
+ - see [table](#architectires) with available model architectures
+ - see [table](#encoders) with avaliable encoders and its corresponding weights
+
+**Configure data preprocessing**
+
+All encoders have pretrained weights. Preparing your data the same way as during weights pretraining may give your better results (higher metric score and faster convergence). But it is relevant only for 1-2-3-channels images and **not necessary** in case you train the whole model, not only decoder.
+
+.. code-block:: python
+
+    from segmentation_models_pytorch.encoders import get_preprocessing_fn
+
+    preprocess_input = get_preprocessing_fn('resnet18', pretrained='imagenet')
+
+
+Congratulations! You are done! Now you can train your model with your favorite framework!