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 OpenAccess] [ArXiv]

Trained models

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

Weights

Backbone	Output dimension	Pitts250k-test			Pitts30k-test			MSLS-val			DOWNLOAD
		R@1	R@5	R@10	R@1	R@5	R@10	R@1	R@5	R@10
ResNet50	4096	94.3	98.2	98.9	91.6	95.5	96.4	88.2	93.1	94.3	LINK
ResNet50	512	93.2	97.9	98.6	90.7	95.5	96.3	84.1	91.8	93.7	LINK
ResNet50	128	88.7	95.8	97.4	87.8	94.3	95.7	78.5	88.2	90.4	LINK

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}
}