MRCNN-Scene-Recognition

by wanglimin

MR-CNNs for Large-Scale Scene Recognition

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Multi-Resolution CNNs for Large-Scale Scene Recognition

Here we provide the code and models for the following paper (Arxiv Preprint):

Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs
Limin Wang, Sheng Guo, Weilin Huang, Yuanjun Xiong, and Yu Qiao 
in IEEE Transactions on Image Processing, 2017

Updates

  • February 21st, 2017
    • Release the code and models
  • January 3rd, 2017
    • Initialize the repo

Overview

We have made two efforts to exploit CNNs for large-scale scene recognition: - We design a modular framework to capture multi-level visual information for scene understanding by training CNNs from different resolutions - We propose a knowledge disambiguation strategy by using soft labels from extra networks to deal with the label ambiguity issue of scene recognition.

These two efforts are the core part of team "SIAT_MMLAB" for the following large-scale scene recogntion challenges.

| Challenge | Rank | Performance | |:-------------------:|:--------------:|:--------------:| | Places2 challenge 2015 | 2nd place | 0.1736 top5-error | | Places2 challenge 2016 | 4th place | 0.1042 top5-error | | LSUN challenge 2015 | 2nd place | 0.9030 top1-accuracy | | LSUN challenge 2016 | 1st place | 0.9161 top1-accuracy |

Places365 Models

We first release the learned models on the Places365 dataset. - Models learned at resolution of 256 * 256

| Model | Top5 Error Rate | |:-------------------:|:--------------:| | (A0) Normal BN-Inception | 0.143 | | (A1) Normal BN-Inception + object networks | 0.141 | | (A2) Normal BN-Inception + scene networks | 0.134 |

  • Models learned at resolution of 384 * 384

| Model | Top5 Error Rate | |:-------------------:|:--------------:| | (B0) Deeper BN-Inception | 0.140 | | (B1) Deeper BN-Inception + object networks | 0.136 | | (B2) Deeper BN-Inception + scene networks | 0.130 |

  • Download initialization and reference models

We release the scripts at the directory of

scripts/
.

Try

bash scripts/get_init_models.sh
to downdload knowldege models.

Try

bash scripts/get_reference_models.sh
to download reference models.

Testing Code

We release the testing code on the Places365 validation dataset at the directory of

matlab/
.

We also release a demo code to use our Places365 model as generic feature extraction and perform scene recognition on the MIT Indoor67 dataset at the directory of

matlab/
.

Training Code

We release the models at the directory of

models/
and the training scripts at the directory of
scripts/
.

Try

bash scripts/256_inception2_train.sh
to train standard CNNs.

Try

bash scripts/256_kd_object_inception2_train.sh
to train knowledge disambiguation networks (by object network).

Try

bash scripts/256_kd_scene_inception2_train.sh
to train knowledge disambiguation netowrks (by scene network).

The training code is based on our modified Caffe toolbox. It is a efficient parallel caffe with MPI implementation. Meanwhile, we implement a new kl-divergence loss layer for our knowledge disambiguation methods;

https://github.com/yjxiong/caffe/tree/kd

Questions

Contact - Limin Wang - Sheng Guo - Weilin Huang

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