This is the PyTorch implementation of our IEEE Signal Processing Letters paper "Learning Semantically Enhanced Feature for Fine-Grained Image Classification". We experimented on 4 fine-grained benchmark datasets --- CUB-200-2011, Stanford Cars, Stanford Dogs, and VGG-Aircraft. You should first download these datasets from their project homepages before runing SEF.
SEF can achieve comparable performance to the state-of-the-art methods with considerably less computation cost and training epochs. It has about 1.7% more parameters than its ResNet-50 backbone on the CUB-Birds dataset and can be easily integrated into other DCNNs.
- PyTorch (>1.5),
- CUDA 10.2
- Python 3.6
- Tensorboard (>2.2.2)
A "x-imdb.py" is provided for each dataset to generate Python pickle files, which are then used to prepare train/val/trainval/test data. Run "x-imdb.py" in the folder of your dataset to generate corresponding pickle file (imdb.pkl) should be the very first step.
-
main.py trains your own SEF model.
-
eval.py outputs classification accuracy by employing pretrained SEF models.
Due to the random generation of train/val/test data on some datasets, the classification accuracy may have a bit fluctuation but it should be in a reasonable range.
The pretrained SEF models can be download from HERE with code i5wk.
SEF-18 and SEF-50 are results of models with different backbones (ResNet-18 and ResNet-50)
| SEF-18 | SEF-50 | |
|---|---|---|
| CUB-200-2011 | 84.8% | 87.3% |
| Stanford Cars | 91.8% | 94.0% |
| Stanford Dogs | 83.1% | 88.8% |
| VGG-Aircraft | 89.3% | 92.1% |
@inproceedings{sef@luowei,
author = {Wei Luo and Hengmin Zhang and Jun Li and Xiu-Shen Wei},
title = {Learning Semantically Enhanced Feature for Fine-Grained Image Classification},
booktitle = {arXiv preprint arXiv:2006.13457},
year = {2020},
}