This is the code of our ICME 2018 paper, "RAM: A Region-Aware Deep Model for Vehicle Re-Identification".
If you find this help, please kindly cite our paper:
@inproceedings{cime-ram-liu,
Author = {Liu, Xiaobin and Zhang, Shiliang and Huang, Qingming and Gao, Wen},<br>
Booktitle = {ICME},
Title = {RAM: A Region-Aware Deep Model for Vehicle Re-Identification},
Year = {2018}
}
You can simply train a RAM on VeRi by running:
sh train_veri.sh
The final model is saved as "snapshot/veri-RAM-finetune_iter_60000.caffemodel". Our models and extracted features on VeRi can be downloaded from here.
We provide a new caffe layer to sample mini-batch. Please refer to "prototxt/train_RAM.prototxt" for an example of usage.
We provide an evaluate script, "evaluate.m", on VeRi following https://github.com/VehicleReId/VeRidataset
We provide a tools in caffe to extract features and write features to binary files. We also provide a tools to read features from binary file, "read_code.m", and a tools to normalize features, "norm_code.m". An example of the usage of these tools can be found in "evaluate.m".
Email: [email protected]
Homepage: https://liu-xb.github.io
Please feel free to contact me if you have any question.