Update README.md

README.md

@@ -24,10 +24,44 @@ tags:
 
 This model demonstrates a road segmentation implemented using **deep learning** techniques which predicts the road regions in the input image and returns it in a grayscale format.
 
-## Model Structure
+## Models
  - **rs1-low.pth**: The lowest performer model with a loss of **0.69%**.
  - **rs1-high.pth**: The highest performer model with a loss of **0.07%**.
 
+## Model Structure
+
+===============================================================================================
+Layer (type:depth-idx)                        Output Shape              Param #
+===============================================================================================
+RoadSegmentationModel                         [16, 1, 256, 256]         --
+├─FeatureListNet: 1-1                         [16, 64, 128, 128]        --
+│    └─Conv2d: 2-1                            [16, 64, 128, 128]        9,408
+│    └─BatchNorm2d: 2-2                       [16, 64, 128, 128]        128
+│    └─ReLU: 2-3                              [16, 64, 128, 128]        --
+│    └─MaxPool2d: 2-4                         [16, 64, 64, 64]          --
+│    └─Sequential: 2-5                        [16, 256, 64, 64]         --
+│    │    └─Bottleneck: 3-1                   [16, 256, 64, 64]         75,008
+│    │    └─Bottleneck: 3-2                   [16, 256, 64, 64]         70,400
+│    │    └─Bottleneck: 3-3                   [16, 256, 64, 64]         70,400
+│    └─Sequential: 2-6                        [16, 512, 32, 32]         --
+│    │    └─Bottleneck: 3-4                   [16, 512, 32, 32]         379,392
+│    │    └─Bottleneck: 3-5                   [16, 512, 32, 32]         280,064
+│    │    └─Bottleneck: 3-6                   [16, 512, 32, 32]         280,064
+│    │    └─Bottleneck: 3-7                   [16, 512, 32, 32]         280,064
+│    └─Sequential: 2-7                        [16, 1024, 16, 16]        --
+│    │    └─Bottleneck: 3-8                   [16, 1024, 16, 16]        1,512,448
+│    │    └─Bottleneck: 3-9                   [16, 1024, 16, 16]        1,117,184
+│    │    └─Bottleneck: 3-10                  [16, 1024, 16, 16]        1,117,184
+│    │    └─Bottleneck: 3-11                  [16, 1024, 16, 16]        1,117,184
+│    │    └─Bottleneck: 3-12                  [16, 1024, 16, 16]        1,117,184
+│    │    └─Bottleneck: 3-13                  [16, 1024, 16, 16]        1,117,184
+...
+Input size (MB): 12.58
+Forward/backward pass size (MB): 4731.17
+Params size (MB): 272.32
+Estimated Total Size (MB): 5016.08
+===============================================================================================
+
 ## Features
 1. ### Architecture
     - Basic **Resnet50** model with few upsampling and batch normalisation layers.