Block | Layer | Kernel size, | Atrous dilation | Output size |
---|---|---|---|---|
 |  | Feature maps | Rate |  |
Input layer | \(192\times 256\times 3\) | – | – | \(192\times 256\times 3\) |
Block 1 | Conv1 | \(3\times 3\), 8 | 1 | \(192\times 256\times 8\) |
Block 2 | Conv2_1 | \(3\times 3\), 16 | 2 | \(192\times 256\times 16\) |
 | Conv2_2 | \(3\times 3\), 16 | 4 | \(192\times 256\times 16\) |
Block 3 | Conv3_1 | \(3\times 3\), 32 | 4 | \(192\times 256\times 32\) |
 | Conv3_2 | \(1\times 1\), 16 | 6 | \(192\times 256\times 16\) |
 | Conv3_3 | \(3\times 3\), 32 | 8 | \(192\times 256\times 32\) |
Block 4 | Conv4_1 | \(3\times 3\), 64 | 8 | \(192\times 256\times 64\) |
 | Conv4_2 | \(1\times 1\), 32 | 10 | \(192\times 256\times 32\) |
 | Conv4_3 | \(3\times 3\), 64 | 10 | \(192\times 256\times 64\) |
 | Conv4_4 | \(3\times 3\), 64 | 12 | \(192\times 256\times 64\) |
Block 5 | Conv5_1 | \(3\times 3\), 128 | 12 | \(192\times 256\times 128\) |
 | Conv5_2 | \(1\times 1\), 64 | 12 | \(192\times 256\times 64\) |
 | Conv5_3 | \(3\times 3\), 128 | 14 | \(192\times 256\times 128\) |
 | Conv5_4 | \(1\times 1\), 64 | 14 | \(192\times 256\times 64\) |
 | Conv5_5 | \(3\times 3\), 128 | 14 | \(192\times 256\times 128\) |
 | final_Conv | \(1\times 1\), 2 | – | \(192\times 256\times 2\) |
 | Softmax | – | – | \(192\times 256\times 2\) |
OutPutMap | Pixel classification | Cross entropy | Loss function | \(192\times 256\times 2\) |