/CRU-Net

Collective Residual Networks

This repository contains the code and trained models of "Sharing Residual Units Through Collective Tensor Factorization in Deep Neural Networks".

Implementation

Augmentation

Method Settings
Random Mirror True
Random Crop 8% - 100%
Aspect Ratio 3/4 - 4/3
Random HSL [20,40,50]

Note: We did not use PCA Lighting and any other advanced augmentation methods.

Normalization

The augmented input images are substrated by mean RGB = [ 124, 117, 104 ], and then multiplied by 0.0167.

Results

ImageNet-1k

Single crop validation error (center 224x224 crop from resized image with shorter side=256):

Model Setting Model Size Top-1
CRU-Net-56 @x14 32x4d 98MB 21.9%
CRU-Net-56 @x14 136x1d 98MB 21.7%
CRU-Net-116 @x28x14 32x4d 168MB 20.6%
CRU-Net-116, wider @x28x14 64x4d 318MB 20.3%

We also trained a tiny CRU-Net-56 with less than half the size of ResNet-50.

Single crop validation error (center 224x224 crop from resized image with shorter side=256):

Model Setting Model Size Top-1
CRU-Net-56,tiny @x14 32x4d 48MB 22.9%

Places365-Standard

10-crop validation accuracy (averaging softmax scores of 10 224x224 crops from resized image with shorter side=256):

Model Setting Model Size Top-1
CRU-Net-116 @x28x14 32x4d 163MB 56.6%

Trained Models

Model Setting Dataset Link
CRU-Net-56,tiny @x14 32x4d ImageNet-1k GoogleDrive
CRU-Net-56 @x14 32x4d ImageNet-1k GoogleDrive
CRU-Net-56 @x14 136x1d ImageNet-1k GoogleDrive
CRU-Net-116 @x28x14 32x4d ImageNet-1k GoogleDrive
CRU-Net-116 @x28x14 32x4d Places365-Standard GoogleDrive