Fast MPN-COV (i.e., iSQRT-COV)

Created by Jiangtao Xie and Peihua Li

      

Introduction

This repository contains the source code under PyTorch framework and models trained on ImageNet 2012 dataset for the following paper:

     @InProceedings{Li_2018_CVPR,
           author = {Li, Peihua and Xie, Jiangtao and Wang, Qilong and Gao, Zilin},
           title = {Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization},
           booktitle = { IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR)},
           month = {June},
           year = {2018}
     }

This paper concerns an iterative matrix square root normalization network (called fast MPN-COV), which is very efficient, fit for large-scale datasets, as opposed to its predecessor (i.e., MPN-COV published in ICCV17) that performs matrix power normalization by Eigen-decompositon. The code on bilinear CNN (B-CNN), compact bilinear pooling and global average pooling etc. is also released for both training from scratch and finetuning. If you use the code, please cite this fast MPN-COV work and its predecessor (i.e., MPN-COV).

Classification results

Classification results (single crop 224x224, %) on ImageNet 2012 validation set

Network	Top-1 Error		Top-5 Error		Pre-trained models
	paper	reproduce	paper	reproduce	GoogleDrive	BaiduCloud
fast MPN-COV-VGG-D	26.55	23.98	8.94	7.12	650.4MB	650.4MB
fast MPN-COV-ResNet50	22.14	21.71	6.22	6.13	217.3MB	217.3MB
fast MPN-COV-ResNet101	21.21	20.99	5.68	5.56	289.9MB	289.9MB

Fine-grained classification results (top-1 accuracy rates, %)

Backbone model	Dim.	Birds		Aircrafts		Cars
		paper	reproduce	paper	reproduce	paper	reproduce
VGG-D	32K	87.2	87.0	90.0	91.7	92.5	93.2
ResNet-50	32K	88.1	88.0	90.0	90.3	92.8	92.3
ResNet-101	32K	88.7	TODO	91.4	TODO	93.3	TODO

• Our method uses neither bounding boxes nor part annotations
• The reproduced results are obtained by simply finetuning our pre-trained fast MPN-COV-ResNet model with a small learning rate, which do not perform SVM as our paper described.

Implementation details

We implement our Fast MPN-COV (i.e., iSQRT-COV) meta-layer under PyTorch package. Note that though autograd package of PyTorch 0.4.0 or above can compute correctly gradients of our meta-layer, that of PyTorch 0.3.0 fails. As such, we decide to implement the backpropagation of our meta-layer without using autograd package, which works well for both PyTorch release 0.3.0 and 0.4.0.

For making our Fast MPN-COV meta layer can be added in a network conveniently, we reconstruct pytorch official demo imagenet/ and models/. In which, we divide any network for three parts: 1) features extractor; 2) global image representation; 3) classifier. As such, we can arbitrarily combine a network with our Fast MPN-COV or some other global image representation methods (e.g.,Global average pooling, Bilinear pooling, Compact bilinear pooling, etc.) Based on these, we can:

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• Finetune a pre-trained model on any image classification datasets.

AlexNet, VGG, ResNet, Inception, etc.

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• Finetune a pre-trained model with a powerful global image representation method on any image classification datasets.

Fast MPN-COV, Bilinear Pooling (B-CNN), Compact Bilinear Pooling (CBP), etc.

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• Train a model from scratch with a powerful global image representation method on any image classification datasets.

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Finetune demo and Train from scratch demo

Welcome to contribution. In this repository, we will keep updating for containing more networks and global image representation methods.

Created and Modified

├── main.py
├── imagepreprocess.py
├── functions.py
├── model_init.py
├── src
│   ├── network
│   │   ├── __init__.py
│   │   ├── base.py
│   │   ├── inception.py
│   │   ├── alexnet.py
│   │   ├── mpncovresnet.py
│   │   ├── resnet.py
│   │   └── vgg.py
│   ├── representation
│   │   ├── __init__.py
│   │   ├── MPNCOV.py
│   │   ├── GAvP.py
│   │   ├── BCNN.py
│   │   ├── CBP.py
│   │   └── Custom.py
│   └── torchviz
│       ├── __init__.py
│       └── dot.py
├── trainingFromScratch
│       └── train.sh
└── finetune
        ├── finetune.sh
        └── two_stage_finetune.sh

For more convenient training and finetuning, we

• implement some functions for plotting convergence curve.
• adopt network visualization tool pytorchviz for plotting network structure.
• use shell file to manage the process.

Installation and Usage

1. Install PyTorch (0.4.0 or above)
2. type git clone https://github.com/jiangtaoxie/fast-MPN-COV
3. pip install -r requirements.txt
4. prepare the dataset as follows

.
├── train
│   ├── class1
│   │   ├── class1_001.jpg
│   │   ├── class1_002.jpg
|   |   └── ...
│   ├── class2
│   ├── class3
│   ├── ...
│   ├── ...
│   └── classN
└── val
    ├── class1
    │   ├── class1_001.jpg
    │   ├── class1_002.jpg
    |   └── ...
    ├── class2
    ├── class3
    ├── ...
    ├── ...
    └── classN

for training from scracth

1. cp trainingFromScratch/train.sh ./
2. modify the dataset path in train.sh
3. sh train.sh

for finetuning our fast MPN-COV model

1. cp finetune/finetune.sh ./
2. modify the dataset path in finetune.sh
3. sh finetune.sh

for finetuning VGG-model by using BCNN

1. cp finetune/two_stage_finetune.sh ./
2. modify the dataset path in two_stage_finetune.sh
3. sh two_stage_finetune.sh

Other Implementations

1. MatConvNet Implementation
2. TensorFlow Implemention

Contact

If you have any questions or suggestions, please contact me

jiangtaoxie@mail.dlut.edu.cn