This is PyTorch implementation of our ICCV 2019 paper "Cross-X Learning for Fine-Grained Visual Categorization". We experimented on 5 fine-grained benchmark datasets --- NABirds, CUB-200-2011, Stanford Cars, Stanford Dogs, and VGG-Aircraft. You should first download these datasets from their project homepages before runing CrossX.
Our implementation is based on PyTorch(>1.0), CUDA 9.0, and Python 3.5.
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.
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demo.py is used to train your own CrossX model from scratch.
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prediction.py outputs classification accuracy by employing pretrained CrossX 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 CrossX models can be download from HERE. If you plan to train your own CrossX model from scratch by using the SENet backbone, you need to download the pretrained SENet-50 weights from HERE.
| CrossX-SENet-50 | CrossX-ResNet-50 | |
|---|---|---|
| NABirds | 86.4% | 86.2% |
| CUB-200-2011 | 87.5% | 87.7% |
| Stanford Cars | 94.5% | 94.6% |
| Stanford Dogs | 88.2% | 88.9% |
| VGG-Aircraft | 92.7% | 92.6% |
If you use CrossX in your research, please cite the paper:
@inproceedings{luowei@19iccv,
author = {Wei Luo and Xitong Yang and Xianjie Mo and Yuheng Lu and Larry S. Davis and Ser-Nam Lim},
title = {Cross-X learning for fine-grained visual categorization},
booktitle = {ICCV},
year = {2019},
}