OpenIBL is an open-source PyTorch-based codebase for image-based localization, or in other words, place recognition. It supports multiple state-of-the-art methods, and also covers the official implementation for our ECCV-2020 spotlight paper SFRS. We support single/multi-node multi-gpu distributed training and testing, launched by slurm or pytorch.
- SFRS: Self-supervising Fine-grained Region Similarities for Large-scale Image Localization (ECCV'20 Spotlight) [paper] [Blog(Chinese)]
- NetVLAD: CNN architecture for weakly supervised place recognition (CVPR'16) [paper] [official code (MatConvNet)]
- SARE: Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization (ICCV'19) [paper] [official code (MatConvNet)]
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How to extract descriptor for a single image? Please refer to Quick Start.
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How to reproduce the results in the paper? Please first refer to INSTALL.md for installation and dataset preparation, and then you can directly test our trained models (downloaded from MODEL_ZOO.md) referring to REPRODUCTION.md/Test, or you can train your own models referring to REPRODUCTION.md/Train.
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How to add my custom dataset for training and testing? Please refer to INSTALL.md/Use Custom Dataset.
import torch
from torchvision import transforms
from PIL import Image
# load the best model with PCA (trained by our SFRS)
model = torch.hub.load('yxgeee/OpenIBL', 'vgg16_netvlad', pretrained=True).eval()
# read image
img = Image.open('image.jpg').convert('RGB') # modify the image path according to your need
transformer = transforms.Compose([transforms.Resize((480, 640)), # (height, width)
transforms.ToTensor(),
transforms.Normalize(mean=[0.48501960784313836, 0.4579568627450961, 0.4076039215686255],
std=[0.00392156862745098, 0.00392156862745098, 0.00392156862745098])])
img = transformer(img)
# use GPU (optional)
model = model.cuda()
img = img.cuda()
# extract descriptor (4096-dim)
with torch.no_grad():
des = model(img.unsqueeze(0))[0]
des = des.cpu().numpy()Please refer to INSTALL.md for installation and dataset preparation.
To reproduce the results in papers, you could train and test the models following the instruction in REPRODUCTION.md.
Please refer to MODEL_ZOO.md for trained models.
OpenIBL is released under the MIT license.
If you find this repo useful for your research, please consider citing the paper
@inproceedings{ge2020self,
title={Self-supervising Fine-grained Region Similarities for Large-scale Image Localization},
author={Yixiao Ge and Haibo Wang and Feng Zhu and Rui Zhao and Hongsheng Li},
booktitle={European Conference on Computer Vision}
year={2020},
}
The structure of this repo is inspired by open-reid, and part of the code is inspired by pytorch-NetVlad.