Table 5.

Body architecture of the developed MobileNet (Howard et al., 2017) for autism image recognition.

Input size	Filter	Layer	Stride
224 * 224 * 3	3 * 3 * 3 * 32	Convolution	S2
112 * 112 * 32	Depth-Wise 3 * 3 * 32	Depth-Wise Convolution	S1
112 * 112 * 32	1 * 1 * 32 * 64	Convolution	S1
112 * 112 * 64	Depth-Wise 3 * 3 * 64	Depth-Wise Convolution	S2
56 * 56 * 64	1 * 1 * 64 * 128	Convolution	S1
56 * 56 * 128	Depth-Wise 3 * 3 * 128	Depth-Wise Convolution	S1
56 * 56 * 128	1 * 1 * 128 * 128	Convolution	S1
56 * 56 * 128	Depth-wise 3 * 3 * 128	Depth-Wise Convolution	S2
28 * 28 * 128	1 * 1 * 128 * 256	Convolution	S1
28 * 28 * 256	Depth-Wise 3 * 3 * 256	Depth-Wise Convolution	S1
28 * 28 * 256	1 * 1 * 256 * 256	Convolution	S1
28 * 28 * 256	Depth-Wise 3 * 3 * 256	Depth-Wise Convolution	S2
14 * 14 * 256	1 * 1 * 256 * 512	Convolution	S1
14 * 14 * 512	Depth-Wise 3 * 3 * 512	Depth-Wise Convolution	S1
14 * 14 * 512	1 * 1 * 512 * 512	Convolution	S1
14 * 14 * 512	Depth-Wise 3 * 3 * 512	Depth-Wise Convolution	S1
14 * 14 * 512	1 * 1 * 512 * 512	Convolution	S1
14 * 14 * 512	Depth-Wise 3 * 3 * 512	Depth-Wise Convolution	S1
14 * 14 * 512	1 * 1 * 512 * 512	Convolution	S1
14 * 14 * 512	Depth-Wise 3 * 3 * 512	Depth-Wise Convolution	S1
14 * 14 * 512	1 * 1 * 512 * 512	Convolution	S1
14 * 14 * 512	Depth-Wise 3 * 3 * 512	Depth-Wise Convolution	S1
14 * 14 * 512	1 * 1 * 512 * 512	Convolution	S1
14 * 14 * 512	Depth-Wise 3 * 3 * 512	Depth-Wise Convolution	S2
7 * 7 * 512	1 * 1 * 512 * 1,024	Convolution	S1
7 * 7 * 1,024	Depth-Wise 3 * 3 * 1,024	Depth-Wise Convolution	S2
7 * 7 * 1,024	Depth-Wise 1 * 1 * 1,024	Convolution	S1
7 * 7 * 1,024	Pool 7 * 7	Average pooling	S1
1 * 1 * 1,024	1,024 * 1,000	Fully connected	S1
1 * 1 * 1,000	Classifier	Softmax	S1