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README.md

README.md

Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding

Introduction

This repository contains the pytorch codes, trained models, and datasets described in the paper "Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding".

For more details, please visit our project page.

Results

  • Accuracy on Caltech UCSD Birds
order family genus class
baseline 98.8 95.0 91.5 85.2
HSE(ours) 98.8 95.7 92.7 88.1
  • Accuracy on Butterfly200
family subfamily genus species
baseline 98.9 97.6 94.8 85.1
HSE(ours) 98.9 97.7 95.4 86.1
  • Accuracy on Vegfru
sup sub
baseline 90.0 87.1
HSE(ours) 90.0 89.4

Installation

Requirement

  • pytorch, tested on v0.4.0
  • CUDA, tested on v8.0
  • Language: Python 2.7

1. Clone the repository

Clone the Hierarchical Semantic Embedding project by:

git clone https://github.com/HCPLab-SYSU/HSE.git

and we denote the folder hse-mm2018 as $HSE_ROOT.

Note that, the correct structure of $HSE_ROOT is like:

hse-mm2018
.
├── code
│   ├── Butterfly200
│   │   ├── baseline
│   │   └── HSE
│   ├── CUB_200_2011
│   │   ├── baseline
│   │   └── HSE
│   └── Vegfru
│       ├── baseline
│       └── HSE
├── data
│   ├── Butterfly200
│   │   └── images
│   ├── CUB_200_2011
│   │   └── images
│   └── Vegfru
│       └── images
├── models
│   ├── Butterfly200
│   ├── CUB_200_2011
│   └── Vegfru
└── scripts

2. Download datasets

Caltech UCSD Birds originally covers 200 classes of birds, and we extend this dataset with a four-level category hierarchy.

Butterfly 200 is constructed in our paper, it also cover four-level categories.

Vegfru is proposed by Hou et al., ICCV2017, and it covers two-level categories.

3. Download trained models

The trained models of our HSE framework and the baseline methods on the extended Caltech UCSD birds, Butterfly-200, and VegFru datasets can be downloaded from OneDrive or Baidu Cloud.

4. Deployment

Firstly, make sure the working directory is $HSE_ROOT, or

cd $HSE_ROOT

then, run the deployment script:

deploy HSE

./scripts/deploy_hse.sh [GPU_ID] [DATASET]
----
GPU_ID: required, 0-based int, 
DATASET: required, 'CUB_200_2011' or 'Butterfly200' or 'Vegfru'

deploy baseline

./scripts/deploy_baseline.sh [GPU_ID] [DATASET] [LEVEL]
----
GPU_ID: required, 0-based int, 
DATASET: required, 'CUB_200_2011' or 'Butterfly200' or 'Vegfru'
LEVEL: require, 
    CUB_200_2011: LEVEL is chosen in ['order', 'family', 'genus', 'class']
    Butterfly200: LEVEL is chosen in ['family', 'subfamily', 'genus', 'species']
    Vegfru: LEVEL is chosen in ['sup', 'sub']

License

The code is released under the SYSU License (refer to the LICENSE file for details). The Human Cyber Physical Intelligence Integration Lab owns this project.

Citing

@inproceedings{chen2018fine,
    Author = {Tianshui Chen, Wenxi Wu, Yuefang Gao, Le Dong, Xiaonan Luo, Liang Lin},
    Title = {Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding},
    Booktitle = {Proc. of ACM International Conference on Multimedia (ACM MM)},
    Year = {2018}
} 

Contributing

For any questions, feel free to open an issue or contact us. (tianshuichen@gmail.com or ngmanhei@foxmail.com)

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