我可以为VGGNet计算500 x 500输入图像的感受野大小。
感受野大小如下。
Layer Name = conv1, Output size = 500, Stride = 1, RF size = 3
Layer Name = relu1_1, Output size = 500, Stride = 1, RF size = 3
Layer Name = conv1_2, Output size = 500, Stride = 1, RF size = 5
Layer Name = relu1_2, Output size = 500, Stride = 1, RF size = 5
Layer Name = pool1, Output size = 250, Stride = 2, RF size = 6
Layer Name = conv2_1, Output size = 250, Stride = 2, RF size = 10
Layer Name = relu2_1, Output size = 250, Stride = 2, RF size = 10
Layer Name = conv2_2, Output size = 250, Stride = 2, RF size = 14
Layer Name = relu2_2, Output size = 250, Stride = 2, RF size = 14
Layer Name = pool2, Output size = 125, Stride = 4, RF size = 16
Layer Name = conv3_1, Output size = 125, Stride = 4, RF size = 24
Layer Name = relu3_1, Output size = 125, Stride = 4, RF size = 24
Layer Name = conv3_2, Output size = 125, Stride = 4, RF size = 32
Layer Name = relu3_2, Output size = 125, Stride = 4, RF size = 32
Layer Name = conv3_3, Output size = 125, Stride = 4, RF size = 40
Layer Name = relu3_3, Output size = 125, Stride = 4, RF size = 40
Layer Name = pool3, Output size = 62, Stride = 8, RF size = 44
Layer Name = conv4_1, Output size = 62, Stride = 8, RF size = 60
Layer Name = relu4_1, Output size = 62, Stride = 8, RF size = 60
Layer Name = conv4_2, Output size = 62, Stride = 8, RF size = 76
Layer Name = relu4_2, Output size = 62, Stride = 8, RF size = 76
Layer Name = conv4_3, Output size = 62, Stride = 8, RF size = 92
Layer Name = relu4_3, Output size = 62, Stride = 8, RF size = 92
Layer Name = pool4, Output size = 31, Stride = 16, RF size = 100
Layer Name = conv5_1, Output size = 31, Stride = 16, RF size = 132
Layer Name = relu5_1, Output size = 31, Stride = 16, RF size = 132
Layer Name = conv5_2, Output size = 31, Stride = 16, RF size = 164
Layer Name = relu5_2, Output size = 31, Stride = 16, RF size = 164
Layer Name = conv5_3, Output size = 31, Stride = 16, RF size = 196
Layer Name = relu5_3, Output size = 31, Stride = 16, RF size = 196
我只看到conv5_3。
例如，如果我的对象大小为150 x 150，而我的图像大小为500 x 500。
我可以这么说，从conv1到conv4_2的早期图层的特征图仅包含我的对象的部分特征，从conv5_2到conv5_3，它们具有几乎整个对象的特征。
我的考虑是否正确？
但是在conv5_3，我的output_size只有31 x 31，所以我无法看到它如何表示图像中的整个对象，但该conv5_3图层中的每个像素代表原始500 x的196 x 196大小500张图片。
我的考虑是否正确？