eBayProduct Retrieval Baseline
This repository contains all code and implementations used in:
eBay eProduct Visual Search Challenge - FGVC8 (CVPR2021)
If you use this code in your research, please cite
@article{Li_Min_Song_Zhu_Kang_Wei_Wei_Jiang_2022,
title={Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones Is Enough},
volume={36},
url={https://ojs.aaai.org/index.php/AAAI/article/view/20042},
DOI={10.1609/aaai.v36i2.20042},
number={2},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={Li, Zhuo and Min, Weiqing and Song, Jiajun and Zhu, Yaohui and Kang, Liping and Wei, Xiaoming and Wei, Xiaolin and Jiang, Shuqiang},
year={2022},
month={Jun.},
pages={1518-1526}
}
- PyTorch 1.2.0+
- Python 3.6+
- torchvision 0.3.0+
- numpy, PIL, oprncv-python
- timm
Data for
- eBay eProduct
[I.] Advanced Runs:
python main_efficient.py --loss triplet --bs 512 --net efficientnet-b4 --cls-num 20000 --lamda 0.9 --lr 1e-4 --loss triplet --batch_mining distance
- To use specific parameters that are loss, batchminer or e.g. datasampler-related, simply set the respective flag.
- For structure and ease of use, parameters relating to a specifc loss function/batchminer etc. are marked as e.g.
--loss_<lossname>_<parameter_name>, seeparameters.py. - However, every parameter can be called from every class, as all parameters are stored in a shared namespace that is passed to all methods. This makes it easy to create novel fusion losses and the likes.
- smoothap [Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval]
--loss smoothap - PNP loss [Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones Is Enough]
--loss pnp - Contrastive []
--loss contrastive - Triplet []
--loss triplet
...
- ResNet50&101 [Deep Residual Learning for Image Recognition] e.g.
--net resnet50&101. - Efficientnet e.g.
--net efficientnet-b4. - Vision Transformer e.g.
--net vit_base_patch16_224.