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Advances, Systems and Applications

Table 1 Overall architecture

From: Lightweight image classifier using dilated and depthwise separable convolutions

Type Filter shape Stride Input size
Dilated Conv 3×3×32 1 224×224×3
Depthwise 3×3×32 2 224×224×32
Separable Conv 1×1×64 1 112×112×32
Dilated Conv 3×3×64 1 112×112×64
Depthwise 3×3×64 2 56×56×64
Separable Conv 1×1×128 1 56×56×64
Dilated Conv 3×3×128 1 1×1×128
Depthwise 3×3×128 1 56×56×128
Separable Conv 1×1×128 1 56×56×128
Dilated Conv 3×3×128 1 56×56×128
Depthwise 3×3×128 2 28×28×128
Separable Conv 1×1×256 1 28×28×128
Dilated Conv 3×3×256 1 28×28×256
Depthwise 3×3×256 1 28×28×256
Separable Conv 1×1×256 1 28×28×256
Dilated Conv 3×3×256 1 28×28×256
Depthwise 3×3×256 2 14×14×256
Separable Conv 1×1×512 1 14×14×256
Dilated Conv 3×3×512 1 14×14×512
Depthwise 3×3×512 1 14×14×512
Separable Conv 1×1×512 1 14×14×512
Dilated Conv 3×3×512 1 14×14×512
Depthwise 3×3×512 2 14×14×512
Separable Conv 1×1×1024 1 7×7×512
Dilated Conv 3×3×1024 1 7×7×1024
Depthwise 3×3×1024 1 7×7×1024
Separable Conv 1×1×1024 1 7×7×1024
Avg Pool 7×7 1 7×7×1024
FC 1024×class 1 1×1×1024
softmax Classifier 1 1×1×class