Table 4 ResNet architectures: Layer-By-Layer specifications35.
From: ResNet-based image processing approach for precise detection of cracks in photovoltaic panels
layer name | output size | 18-layer | 34-layer | 50-layer | 101-layer | 152-layer |
|---|---|---|---|---|---|---|
conv1 | \(\:112\times\:112\) | 7 × 7,64 stride 2 | ||||
conv2_x | \(\:56\times\:56\) | 3 × 3 max pool, stride 2 | ||||
\(\:\left[\begin{array}{c}3\times\:3,\:64\\\:3\times\:3,\:64\end{array}\right]\times\:2\) | \(\:\left[\begin{array}{c}3\times\:3,\:64\\\:3\times\:3,\:64\end{array}\right]\times\:3\) | \(\:\left[\begin{array}{c}1\times\:1,\:64\\\:3\times\:3,\:64\\\:1\times\:1,\:256\end{array}\right]\times\:3\) | \(\:\left[\begin{array}{c}1\times\:1,\:64\\\:3\times\:3,\:64\\\:1\times\:1,\:256\end{array}\right]\times\:3\) | \(\:\left[\begin{array}{c}1\times\:1,\:64\\\:3\times\:3,\:64\\\:1\times\:1,\:256\end{array}\right]\times\:3\) | ||
conv3_x | \(\:28\times\:28\) | \(\:\left[\begin{array}{c}3\times\:3,\:128\\\:3\times\:3,\:128\end{array}\right]\times\:2\) | \(\:\left[\begin{array}{c}3\times\:3,\:128\\\:3\times\:3,\:128\end{array}\right]\times\:4\) | \(\:\left[\begin{array}{c}1\times\:1,\:128\\\:3\times\:3,\:128\\\:1\times\:1,\:512\end{array}\right]\times\:4\) | \(\:\left[\begin{array}{c}1\times\:1,\:128\\\:3\times\:3,\:128\\\:1\times\:1,\:512\end{array}\right]\times\:4\) | \(\:\left[\begin{array}{c}1\times\:1,\:128\\\:3\times\:3,\:128\\\:1\times\:1,\:512\end{array}\right]\times\:8\) |
conv4_x | \(\:14\times\:14\) | \(\:\left[\begin{array}{c}3\times\:3,\:256\\\:3\times\:3,\:256\end{array}\right]\times\:2\) | \(\:\left[\begin{array}{c}3\times\:3,\:256\\\:3\times\:3,\:256\end{array}\right]\times\:6\) | \(\:\left[\begin{array}{c}1\times\:1,\:256\\\:3\times\:3,\:256\\\:1\times\:1,\:1024\end{array}\right]\times\:6\) | \(\:\left[\begin{array}{c}1\times\:1,\:256\\\:3\times\:3,\:256\\\:1\times\:1,\:1024\end{array}\right]\times\:23\) | \(\:\left[\begin{array}{c}1\times\:1,\:256\\\:3\times\:3,\:256\\\:1\times\:1,\:1024\end{array}\right]\times\:36\) |
conv5_x | \(\:7\times\:7\) | \(\:\left[\begin{array}{c}3\times\:3,\:512\\\:3\times\:3,\:512\end{array}\right]\times\:2\) | \(\:\left[\begin{array}{c}3\times\:3,\:512\\\:3\times\:3,\:512\end{array}\right]\times\:3\) | \(\:\left[\begin{array}{c}1\times\:1,\:512\\\:3\times\:3,\:512\\\:1\times\:1,\:2048\end{array}\right]\times\:3\) | \(\:\left[\begin{array}{c}1\times\:1,\:512\\\:3\times\:3,\:512\\\:1\times\:1,\:2048\end{array}\right]\times\:3\) | \(\:\left[\begin{array}{c}1\times\:1,\:512\\\:3\times\:3,\:512\\\:1\times\:1,\:2048\end{array}\right]\times\:3\) |
\(\:1\times\:1\) | Average pool, 1000-d fc, softmax (classification head) | |||||
FLOPs (Floating Point Operations per Second) | \(\:1.8\times\:{10}^{9}\) | \(\:3.6\times\:{10}^{9}\) | \(\:3.8\times\:{10}^{9}\) | \(\:7.6\times\:{10}^{9}\) | \(\:11.3\times\:{10}^{9}\) | |
