This is a Keras implementation of the NetVLAD architecture for visual place recognition. The main purpose of this project is to provide an easy to use (and trainable) Keras model.
The original Netvlad model was written in matlab (Github page) and later transfered to Tensorflow (Github page).
The model can be found in netvlad_keras.py along with the custom layer in netvladlayer.py. The latter is adapted from the tensorflow version. The file savemodel.py sets the weights from the tensorflow checkpoint to the keras model and stores them. Since the main work was to get these weights from matlab to tensorflow, I encourage everyone using this repository to also cite the author's paper:
T. Cieslewski, S. Choudhary, D. Scaramuzza: Data-Efficient Decentralized Visual SLAM IEEE International Conference on Robotics and Automation (ICRA), 2018.
In addition, compare.py checks wheather the outputs of the two models are consistent between Tensorflow and Keras for a given dataset.
check_layers.py is a low level debugging script which allows you to compare the two model's outputs for each layer.
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Download the weights from here or create them on your own using
savemodel.py. -
Create the model and load the weights
from netvlad_keras import NetVLADModel model = NetVLADModel() model.load_weights('netvlad_weights.h5') model.build() -
Get the output for a batch of images
output = model.predict(batch)
A working example can be found in example.py
A few results showing the best match in a dataset given the query image
- Currently, the model cannot be loaded from a file because of the custom layers.
- The loss function has to be implemented (the model is not trainable yet)