MixVPR: Feature Mixing for Visual Place Recognition

This is the official repo for WACV 2023 paper "MixVPR: Feature Mixing for Visual Place Recognition"

Summary

This paper introduces MixVPR, a novel all-MLP feature aggregation method that addresses the challenges of large-scale Visual Place Recognition, while remaining practical for real-world scenarios with strict latency requirements. The technique leverages feature maps from pre-trained backbones as a set of global features, and integrates a global relationship between them through a cascade of feature mixing, eliminating the need for local or pyramidal aggregation. MixVPR achieves new state-of-the-art performance on multiple large-scale benchmarks, while being significantly more efficient in terms of latency and parameter count compared to existing methods.

[WACV open access] [ArXiv]

Trained models

All models have been trained on GSV-Cities dataset (https://github.com/amaralibey/gsv-cities).

Weights

Code to load the pretrained weights is as follows:

from main import VPRModel

# Note that images must be resized to 320x320
model = VPRModel(backbone_arch='resnet50', 
                 layers_to_crop=[4],
                 agg_arch='MixVPR',
                 agg_config={'in_channels' : 1024,
                             'in_h' : 20,
                             'in_w' : 20,
                             'out_channels' : 1024,
                             'mix_depth' : 4,
                             'mlp_ratio' : 1,
                             'out_rows' : 4},
                )

state_dict = torch.load('./LOGS/resnet50_MixVPR_4096_channels(1024)_rows(4).ckpt')
model.load_state_dict(state_dict)
model.eval()

Bibtex

@inproceedings{ali2023mixvpr,
  title={MixVPR: Feature Mixing for Visual Place Recognition},
  author={Ali-bey, Amar and Chaib-draa, Brahim and Gigu{\`e}re, Philippe},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={2998--3007},
  year={2023}
}