Merge pull request #77 from bkhanal-11/master

added files for unet

Author: vaghawan

_data/authors.yml

@@ -22,6 +22,15 @@ ashuta:
       icon: "fab fa-fw fa-github"
       url: "https://github.com/ashuta03"
 
+bishwash:
+  name    : "Bishwash Khanal"
+  bio     : ""
+  avatar  : "/assets/images/userPhoto/no_photo.jpg"
+  links:
+    - label: "Github"
+      icon: "fab fa-fw fa-github"
+      url: "https://github.com/bkhanal-11"
+
 sajita: 
   name: "Sajita"
   bio: ""

_posts/Applied Machine Learning/2022-10-11-semantic-segmentation-unet.md

@@ -0,0 +1,107 @@
+---
+title: "Semantic Segmentation using U-Net"
+excerpt_separator: "<!--more-->"
+last_modified_at: 2022-10-11T14:36:02-05:00
+categories:
+  - Applied Machine Learning
+tags:
+- U-Net
+- Semantic Segmentation
+- Dice Loss
+header:
+    image: /assets/images/unet/semantic.jpeg
+    image_description: "A description of the image"
+author: bishwash
+---
+
+## U-Net
+
+U-Net is a u-shaped encoder-decoder network architecture, which consists of four encoder blocks 
+and four decoder blocks that are connected via bridge. It is one of the most popularly used approaches 
+in any semantic segmentation task. It was originally introduced by Olaf Ronneberger through the publication
+"U-Net: Convolutional Networks for Biomedical Image Segmentation". It is a fully convolutional neural network 
+that is designed to learn from fewer training samples.
+
+<p align="center">
+    <img src="{{ site.url }}{{ site.baseurl }}/assets/images/unet/unet-arc.png" width="600" />
+
+</p>
+ 
+It has three main componets namely encoder network, decoder network and skip connections. The encoder network 
+(contracting path) halfs the spatial dimensions and doubles the number of feature channels at each encoder block 
+while the decoder network doubles the spatial dimensions and halfs the number of of feature channels. The skip 
+connections connect output of encoder block with corresponding input of decoder block.
+
+
+### Encoder Network
+
+Encoder Network acts as the feature extractor and learn an abstract representation of the input image through 
+a sequence of the encoder blocks. Each encoder block consists of 3x3 convolutions where each convolution is followed by 
+a ReLU (Rectified Linear Unit) activation function. The ReLU function introduces non-linearity into the network, which 
+helps in the better generalization of the training data. The output of ReLU acts as a skip connection for the corresponding 
+decoder block.
+
+Next follows a 2x2 max-pooling, where the spatial dimensions of the feature maps are reduced by half. This reduces the 
+computational cost by decreasing the number of trainable parameters.
+
+### Skip Connection
+
+These skip connections provide additional information that helps the decoder generate better semantic features. They also act 
+as a shortcut connection that helps the indirect flow of gradients to the earlier layers without any degradation. 
+
+The bridge connects the encoder and decoder network and completes the flow of information.
+
+### Decoder Network
+
+It is used to take the abstract representation and generate a semantic segmentation mask. The decoder block starts with 2x2 transpose 
+convolution. Next, it is concatenated with the corresponding skip connection feature map from the encoder block. These skip 
+connections provide features from earlier layers that are sometimes lost due to the depth of the network. The output of the last 
+decoder passes through 1x1 convolution with sigmoid activation. The sigmoid activation function gives the segmenation mask representing the 
+pixel-wise classification.
+
+It is prefered to use batch normalization in between the convolution layer and the ReLU activation function. It reduces internal 
+covariance shift and makes the network more stable while training. Sometimes, the dropout is used after ReLU. It forces the network to learn 
+different representation by dropping out some randomly selected neurons. It helps the network to become less dependent upon certain neuron. 
+This in turn helps the network to better generalize and prevent it from overfitting.
+
+
+## Semantic Segmentation for Self Driving Cars
+
+This dataset provides data images and labeled semantic segmentations captured via CARLA self-driving car simulator. The data was 
+generated as part of the Lyft Udacity Challenge . This dataset can be used to train ML algorithms to identify semantic segmentation 
+of cars, roads etc in an image. The data has 5 sets of 1000 images and corresponding labels. There are 23 different labels ranging from 
+road, roadlines, sidewalk to building, pedestrians, fences. 
+
+<p align="center">
+    <img src="{{ site.url }}{{ site.baseurl }}/assets/images/unet/example.png" width="600"/>
+</p>
+
+For our training, we select the first 13 labels. Then all the images were resized to 256x256. The train-validation-test split was 
+0.6, 0.2 and 0.2. Learning rate was chosen to be 0.001 for Adam optimizer. The performance of the network was optimized with the help 
+of Dice Loss which is defined as 
+
+<p align="center">
+    <img src="{{ site.url }}{{ site.baseurl }}/assets/images/unet/diceloss.png" width="350"/>
+</p>
+
+The performance of network for for first 25 epoch out of 100 is as follow.
+
+<p align="center">
+    <img src="{{ site.url }}{{ site.baseurl }}/assets/images/unet/loss.png" width="450"/>
+</p>
+
+Some predicted results for buildings with their ground truth is as follow.
+
+<p align="center">
+    <img src="{{ site.url }}{{ site.baseurl }}/assets/images/unet/results.png" width="600"/>
+</p>
+
+#### References
+
+[1] [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf)
+
+[2] [What is UNET?](https://medium.com/analytics-vidhya/what-is-unet-157314c87634)
+
+[3] [Semantic Segmentation for Self Driving Cars](https://www.kaggle.com/datasets/kumaresanmanickavelu/lyft-udacity-challenge)
+
+[4] [Semantic segmentation of aerial imagery](https://www.kaggle.com/datasets/humansintheloop/semantic-segmentation-of-aerial-imagery)