The Pytorch Implementation of Category-consistent deep network learning for accurate vehicle logo recognition
- This is the offical website for paper ''Category-consistent deep network learning for accurate vehicle logo recognition''.
- Paper: Category-consistent deep network learning for accurate vehicle logo recognition
- Authors: Wanglong Lu, Hanli Zhao, Qi He, Hui Huang, Xiaogang Jin
- Pytorch==1.0.1 or higher
- opencv version: 4.1.0
- XMU:
- Y. Huang, R. Wu, Y. Sun, W. Wang, and X. Ding, Vehicle logo recognition system based on convolutional neural networks with a pretraining strategy, IEEE Transactions on Intelligent Transportation Systems 16 (4) (2015) 1951-1960.
- https://xmu-smartdsp.github.io/VehicleLogoRecognition.html
- HFUT-VL1 and HFUT-VL2:
- Y. Yu, J. Wang, J. Lu, Y. Xie, and Z. Nie, Vehicle logo recognition based on overlapping enhanced patterns of oriented edge magnitudes, Computers & Electrical Engineering 71 (2018) 273–283.
- https://github.com/HFUT-VL/HFUT-VL-dataset
- CompCars:
- L. Yang, P. Luo, C. C. Loy, and X. Tang, A large-scale car dataset for fine-grained categorization and verification, in: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 3973-3981.
- http://mmlab.ie.cuhk.edu.hk/datasets/comp_cars/index.html
- VLD-45:
- S. Yang, C. Bo, J. Zhang, P. Gao, Y. Li and S. Serikawa, VLD-45: A big dataset for vehicle logo recognition and detection, IEEE Transactions on Intelligent Transportation Systems (2021) doi: 10.1109/TITS.2021.3062113.
- https://github.com/YangShuoys/VLD-45-B-DATASET-Detection
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- training XMU dataset
python train.py --dataset_name XMU --framework Classification_Network- training HFUT-VL1 dataset
python train.py --dataset_name HFUT_VL1 --framework Classification_Network- training HFUT-VL2 dataset
python train.py --dataset_name HFUT_VL2 --framework Classification_Network- training CompCars dataset
python train.py --dataset_name CompCars --framework Classification_Network- training VLD-45 dataset
python train.py --dataset_name VLD-45 --framework Classification_Network -
- testing XMU dataset
python test.py --dataset_name XMU --framework Classification_Network- testing HFUT-VL1 dataset
python test.py --dataset_name HFUT_VL1 --framework Classification_Network- testing HFUT-VL2 dataset
python test.py --dataset_name HFUT_VL2 --framework Classification_Network- testing CompCars dataset
python test.py --dataset_name CompCars --framework Classification_Network- testing VLD-45 dataset
python test.py --dataset_name VLD-45 --framework Classification_Network
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- Generation of the category-consistent masks. There are more details for the co-localization method PSOL.
- Please note that we use the generated binary-masks directly instead of the predicted boxes.
1. Download the source codes of [PSOL](https://github.com/tzzcl/PSOL). 2. Add the generate_mask_imagenet.py from our project to the PSOL folder. 3. Run the generate_mask_imagenet.py - For the generate_mask_imagenet.py --input_size input size for each image (448 for default) --gpu which gpus to use --output_path the output pseudo boxes folder --batch_size batch_size for executing forward pass of CNN --output_mask_path the output pseudo mask folder -
- After generating the category-consistent masks, we can further organize the training and testing data which are as below:
Note that each image has the corresponding generated category-consistent mask.root/ test/ dog/xxx.png dog/xxz.png cat/123.png cat/nsdf3.png train/ dog/xxx.png dog/xxz.png cat/123.png cat/nsdf3.png mask/ dog/xxx.png dog/xxz.png cat/123.png cat/nsdf3.png -
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Now, you can training the model with the category-consistent mask learning framework
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- training XMU dataset
python train.py --dataset_name XMU --framework CCML_Network- training HFUT-VL1 dataset
python train.py --dataset_name HFUT_VL1 --framework CCML_Network- training HFUT-VL2 dataset
python train.py --dataset_name HFUT_VL2 --framework CCML_Network- training CompCars dataset
python train.py --dataset_name CompCars --framework CCML_Network- training VLD-45 dataset
python train.py --dataset_name VLD-45 --framework CCML_Network -
- testing XMU dataset
python test.py --dataset_name XMU --framework CCML_Network- testing HFUT-VL1 dataset
python test.py --dataset_name HFUT_VL1 --framework CCML_Network- testing HFUT-VL2 dataset
python test.py --dataset_name HFUT_VL2 --framework CCML_Network- testing CompCars dataset
python test.py --dataset_name CompCars --framework CCML_Network- testing VLD-45 dataset
python test.py --dataset_name VLD-45 --framework CCML_Network
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- If you find our code useful, please cite our paper:
@article{LU2021, title = {Category-consistent deep network learning for accurate vehicle logo recognition}, journal = {Neurocomputing}, year = {2021}, issn = {0925-2312}, doi = {https://doi.org/10.1016/j.neucom.2021.08.030}, url = {https://www.sciencedirect.com/science/article/pii/S0925231221012145}, author = {Wanglong Lu and Hanli Zhao and Qi He and Hui Huang and Xiaogang Jin} }
- The authors would like to thank our anonymous reviewers for their valuable comments to improve this paper.
- Our codes benefit from the official memory-efficient-based DenseNet by @gpleiss, Billion-scale-semi-supervised-learning by @leaderj1001, and the implementation of the category-consistent from PSOL by @tzzcl. Thanks for their beautiful work.
- Thanks @kaijieshi7, @Tao Wang, and @Changzu Chen for their help in building the project.