This is quick evaluation of different colorspace preprocessing performance on ImageNet-2012.
The architecture is similar to CaffeNet, but has differences:
- Images are resized to small side = 128 for speed reasons.
- fc6 and fc7 layers have 2048 neurons instead of 4096.
- Networks are initialized with LSUV-init
- No LRN layers.
| 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 |
