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 |
Requirement
- pytorch, tested on v0.4.0
- CUDA, tested on v8.0
- Language: Python 2.7
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
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.
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.
Firstly, make sure the working directory is $HSE_ROOT, or
cd $HSE_ROOT
then, run the deployment script:
./scripts/deploy_hse.sh [GPU_ID] [DATASET]
----
GPU_ID: required, 0-based int,
DATASET: required, 'CUB_200_2011' or 'Butterfly200' or 'Vegfru'
./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']
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.
@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}
}
For any questions, feel free to open an issue or contact us. ([email protected] or [email protected])
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent