【框架】PyTorch 图像检索框架
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【框架】PyTorch 图像检索框架
1年前 ⋅ 2741 ⋅ 0 ⋅ 2
作者：leeesangwon
GitHub项目：PyTorch-Image-Retrieval
本文介绍了一个PyTorch 图像检索框架，可以很好的实现N-pair Loss (NIPS 2016) 和Angular Loss (CVPR 2017)的图像检索任务
Loss functions
We implemented loss functions to train the network for image retrieval.
Batch sampler for the loss function borrowed from here.
- N-pair Loss (NIPS 2016): Sohn, Kihyuk. "Improved Deep Metric Learning with Multi-class N-pair Loss Objective," Advances in Neural Information
Processing Systems. 2016. - Angular Loss (CVPR 2017): Wang, Jian. "Deep Metric Learning with Angular Loss," CVPR, 2017
Self-attention module
We attached the self-attention module of the Self-Attention GAN to conventional classification networks (e.g. DenseNet, ResNet, or SENet).
Implementation of the module borrowed from here.
Data augmentation
We adopted data augmentation techniques used in Single Shot MultiBox Detector.
Post processing
We utilized the following post-processing techniques in the inference phase.
- Moving the origin of the feature space to the center of the feature vectors
- L2-normalization
- Average query expansion
- Database-side feature augmentation
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