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This is quick evaluation of different colorspace preprocessing performance on ImageNet-2012.

The architecture is similar to CaffeNet, but has differences:

  1. Images are resized to small side = 128 for speed reasons.
  2. fc6 and fc7 layers have 2048 neurons instead of 4096.
  3. Networks are initialized with LSUV-init
  4. No LRN layers.

Colorspace

Name Accuracy LogLoss Comments
RGB 0.471 2.36 default, no changes. Input = 0.04 * (Img - [104, 117,124])
RGB_by_BN 0.469 2.38 Input = BatchNorm(Img)
CLAHE 0.467 2.38 RGB -> LAB -> CLAHE(L)->RGB->BatchNorm(RGB)
HISTEQ 0.448 2.48 RGB -> HiestEq
YCrCb 0.458 2.42 RGB->YCrCb->BatchNorm(YCrCb)
HSV 0.451 2.46 RGB->HSV->BatchNorm(HSV)
Lab - - Doesn`t leave 6.90 loss after 1.5K iters
RGB->10->3 TanH 0.463 2.40 RGB -> conv1x1x10 tanh -> conv1x1x3 tanh
RGB->10->3 VlReLU 0.485 2.28 RGB -> conv1x1x10 vlrelu -> conv1x1x3 vlrelu
RGB->10->3 VlReLU->sum(RGB) 0.482 2.30 RGB -> conv1x1x10 vlrelu -> conv1x1x3 -> sum(RGB) ->vlrelu
RGB and log(RGB)->10->3 VlReLU 0.482 2.29 RGB and log (RGB) -> conv1x1x10 vlrelu -> conv1x1x3 vlrelu
RGB and log(RGB) and log (256-RGB)->10->3 VlReLU 0.484 2.29 RGB and log (RGB) and log (256 - RGB) -> conv1x1x10 vlrelu -> conv1x1x3 vlrelu
RGB->10->3 Maxout 0.488 2.26 RGB -> conv1x1x10 maxout(2) -> conv1x1x3 maxout(2)
RGB->16->3 VlReLU 0.483 2.30 RGB -> conv1x1x16 vlrelu -> conv1x1x3 vlrelu
RGB->3->3 VlReLU 0.480 2.32 RGB -> conv1x1x3 vlrelu -> conv1x1x3 vlrelu
NN-Scale 0.467 2.38 Nearest neightbor instead of linear interpolation for rescale. Faster, but worse :(
concat_rgb_each_pool 0.441 2.51 Concat avepoolRGB with each pool
OpenCV RGB2Gray 0.413 2.70 RGB->Grayscale Gray = 0.299 R + 0.587 G + 0.114 B
Learned RGB2Gray 0.419 2.66 RGB->conv1x1x1. -1.779 *R + 6.511 * G + 1.493 *B + 3.279

Prototxt, logs

CaffeNet128 test accuracy

CaffeNet128 test loss

CaffeNet128 train loss

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