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MODEL_ZOO.md

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Model Zoo

Here we provide the pre-trained models to help you reproduce our experimental results easily.

General image retrieval

pre-trained models

Training Set Backbone for Short Download
ImageNet VGG-16 I-VGG16 model
Places365 VGG-16 P-VGG16 model
ImageNet + Places365 VGG-16 H-VGG16 model
ImageNet ResNet-50 I-Res50 model
Places365 ResNet-50 P-Res50 model
ImageNet + Places365 ResNet-50 H-Res50 model

performance

Dataset Data Augmentation Backbone Pooling Dimension Process mAP
Oxford5k ShorterResize + CenterCrop H-VGG16 GAP l2 +SVD(whiten) + l2 62.9
CUB-200 ShorterResize + CenterCrop I-Res50 SCDA l2 + PCA + l2 27.8
Indoor DirectResize P-Res50 CroW l2 + PCA + l2 51.8
Caltech101 PadResize I-Res50 GeM l2 + PCA + l2 77.9

Choosing the implementations mentioned above as baselines and adding some tricks, we have:

Dataset Implementations mAP
Oxford5k baseline + K-reciprocal 72.9
CUB-200 baseline + K-reciprocal 38.9
Indoor baseline + DBA + QE 63.7
Caltech101 baseline + DBA + QE + K-reciprocal 86.1

Person re-identification

For person re-identification, we use the model provided by Person_reID_baseline and reproduce its resutls. In addition, we train a model on DukeMTMC-reID through the open source code for further experiments.

pre-trained models

Training Set Backbone for Short Download
Market-1501 ResNet-50 M-Res50 model
DukeMTMC-reID ResNet-50 D-Res50 model

performance

Dataset Data Augmentation Backbone Pooling Dimension Process mAP Recall@1
Market-1501 DirectResize + TwoFlip M-Res50 GAP l2 71.6 88.8
DukeMTMC-reID DirectResize + TwoFlip D-Res50 GAP l2 62.5 80.4

Choosing the implementations mentioned above as baselines and adding some tricks, we have:

Dataset Implementations mAP Recall@1
Market-1501 Baseline + l2 + PCA + l2 + K-reciprocal 84.8 90.4
DukeMTMC-reID Baseline + l2 + PCA + l2 + K-reciprocal 78.3 84.2