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