Cross-Resnet

Cross-Resnet ():

C-ResNet18-A	C-ResNet27-A	C-ResNet27-A1	C-ResNet27-A2	C-ResNet27-B	C-ResNet27-C	C-ResNet27-C1

Overview

Here we provide the code of nine cross resnet block in pytorch, along with the four datasets for training in paper . The repository is organised as follows:

• data/ contains two datasets data (Caltech101 and Caltech256), and others' two datasets cifar10 and cifar100 can download from internet easily. Caltech101 source:http://www.vision.caltech.edu/Image_Datasets/Caltech101/ . Caltech256 source:http://www.vision.caltech.edu/Image_Datasets/Caltech256/.
• resnetimprove.py contains the code of the original resnet block (BasicBlock and Bottleneck) and nine cross resnet block ('C_BasicBlock_A1','C_BasicBlock_A','C_BasicBlock_A2','C_Bottleneck_C1','C_Bottleneck_C','c_Bottleneck_B','c_Bottleneck_B1','c_Bottleneck_B2','c_Bottleneck_B3');
• main.py running model training.

Example

We use Cifar10 as the default data set, and the -d option is made available for changing the data set (0 for Cifar10, 1 for Cifar100, 2 for Caltech101, and 3 for Caltech256). The c_BasicBlock_A1 is the default Cross Block, which can be changed with the -b option (0 for BasicBlock,1 for Bottleneck. See the help command output for args in main.py). One can also change the stack structure of Cross blocks with -l option, e.g. -l 2,2,2,2.

Examples for running main.py on terminal:

• If you want to train C-ResNet15-A1 on Cifar10, run it by the command: python main.py -l 1,1,1,1.
• If you want to train C-ResNet18-A on Cifar10, run it by the command: python main.py -b 3 -l 1,2,1,1.
• If you want to train C-ResNet27-A2 on Cifar100, run it by the command: python main.py -d 1 -b 4 -l 2,2,2,2.
• If you want to train C-ResNet27-B on Caltech101(Please download the caltech101 dataset yourself), run it by the command: python main.py -d 2 -b 7 -l 1,1,1,1.
• If you want to train C-ResNet27-C on Caltech256, run it by the command: python main.py -d 3 -b 6 -l 2,2,2,2.

If you want to konw more details about running, you can read the source code: main.py.

Dependencies

The script has been tested running under Python 3.7.4, with the following packages installed (along with their dependencies):

• numpy==1.18.1
• torch==1.5.0
• torchvision==0.6.0

In addition, CUDA 10.2 and cuDNN 7.6.5 have been used. We experimented on Tesla V100.

Pre-trained Models

We provide pre-trained models for some important networks. You can get them on:https://drive.google.com/drive/folders/1ZNm7hdfzjcNoVv-3RGyE8Gf5JavaEL_p?usp=sharing

Reference

If you make advantage of the C-resnet model in your research, please cite the following in your manuscript:

@article{
  
}