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| -rw-r--r-- | examples/net_surgery.ipynb | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/examples/net_surgery.ipynb b/examples/net_surgery.ipynb index d50d503b..217c2d1a 100644 --- a/examples/net_surgery.ipynb +++ b/examples/net_surgery.ipynb @@ -5479,7 +5479,7 @@ "\n", "Let's take the standard Caffe Reference ImageNet model \"CaffeNet\" and transform it into a fully convolutional net for efficient, dense inference on large inputs. This model generates a classification map that covers a given input size instead of a single classification. In particular a 8 $\\times$ 8 classification map on a 451 $\\times$ 451 input gives 64x the output in only 3x the time. The computation exploits a natural efficiency of convolutional network (convnet) structure by amortizing the computation of overlapping receptive fields.\n", "\n", - "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 \\times 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding." + "To do so we translate the `InnerProduct` matrix multiplication layers of CaffeNet into `Convolutional` layers. This is the only change: the other layer types are agnostic to spatial size. Convolution is translation-invariant, activations are elementwise operations, and so on. The `fc6` inner product when carried out as convolution by `fc6-conv` turns into a 6 $\\times$ 6 filter with stride 1 on `pool5`. Back in image space this gives a classification for each 227 $\\times$ 227 box with stride 32 in pixels. Remember the equation for output map / receptive field size, output = (input - kernel_size) / stride + 1, and work out the indexing details for a clear understanding." ] }, { |