论文笔记 Very Deep Convolutional Networks for Large-Scale Visual Recognition - ICLR 2014

VGG Very Deep Convolutional Networks for Large-Scale Visual Recognition

Karen Simonyan and Andrew Zisserman ICLR, 2014 (PDF) (Citations 73354)

论文笔记 Very Deep Convolutional Networks for Large-Scale Visual Recognition - ICLR 2014_第1张图片

Contribution

  • 通过堆叠多个3x3的卷积核来替代大尺度卷积核(减少所需参数,两个3x3的卷积核和一个5x5的卷积核具有相同的感受野,三个3x3的卷积核和一个7x7的卷积核具有相同的感受野)。
  • AlexNet提出的LRN实际用处不大(可以使用BN)。

Details

  • ILSVRC’14 2nd in classification, 1st in localization
  • Use VGG16 or VGG19 (VGG19 only slightly better, more memory)
  • Use ensembles for best results
  • FC7 features generalize well to other tasks

参数计算(VGG16, not counting biases)

Layer input size memory params
INPUT [224×224×3] 224×224×3=150K 0
CONV3-64 [224×224×64] 224×224×64=3.2M (3×3×3)×64=1,728
CONV3-64 [224×224×64] 224×224×64=3.2M (3×3×64)×64=36,864
POOL2 [112×112×64] 112×112×64=800K 0
CONV3-128 [112×112×128] 112×112×128=1.6M (3×3×64)×128=73,728
CONV3-128 [112×112×128] 112×112×128=1.6M (3×3×128)×128=147,456
POOL2 [56×56×128] 56×56×128=400K 0
CONV3-256 [56×56×256] 56×56×256=800K (3×3×128)×256=294,912
CONV3-256 [56×56×256] 56×56×256=800K (3×3×256)×256=589,824
CONV3-256 [56×56×256] 56×56×256=800K (3×3×256)×256=589,824
POOL2 [28×28×256] 28×28×256=200K 0
CONV3-512 [28×28×512] 28×28×512=400K (3×3×256)×512=1,179,648
CONV3-512 [28×28×512] 28×28×512=400K (3×3×512)×512=2,359,296
CONV3-512 [28×28×512] 28×28×512=400K (3×3×512)×512=2,359,296
POOL2 [14×14×512] 14×14×512=100K 0
CONV3-512 [14×14×512] 14×14×512=100K (3×3×512)×512=2,359,296
CONV3-512 [14×14×512] 14×14×512=100K (3×3×512)×512=2,359,296
CONV3-512 [14×14×512] 14×14×512=100K (3×3×512)×512=2,359,296
POOL2 [7×7×512] 7×7×512=25K 0
D [1×1×4096] 4096 7×7×512×4096=102,760,448
FC [1×1×4096] 4096 4096×4096 = 16,777,216
FC [1×1×1000] 1000 4096×1000 = 4,096,000

TOTAL memory: 24M × 4 bytes ≈ 96MB / image (for a forward pass)

TOTAL params: 138M parameters

Notes:

  • Most memory is in early CONV
  • Most params are in late FC

References

  • cs231n

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