Table 1 The structure of 3D-ResNet
From: Classifying the tracing difficulty of 3D neuron image blocks based on deep learning
Stage | Component | Output size |
|---|---|---|
Convolution | \(3\times 3\times 3\), 64, stride(1,2,2) | \(32\times 32\times 32\) |
Max pooling | \(3\times 3\times 3\), 64, stride(2,2,2) | \(16\times 16\times 16\) |
Residual layer 1 | Dropout=0.2, unit-A(64), unit-A(64) | \(16\times 16\times 16\) |
Residual layer 2 | Dropout=0.2, unit-B(128), unit-A(128) | \(8\times 8\times 8\) |
Residual layer 3 | Dropout=0.2, unit-B(256), unit-A(256) | \(4\times 4\times 4\) |
Residual layer 4 | Dropout=0.2, unit-B(512), unit-A(512) | \(2\times 2\times 2\) |
Average pooling | \(2\times 2\times 2\), stride(2,2,2) | \(1\times 1\times 1\) |
Classification layer | Fully-connected, softmax | 2 |