This code is developed based on pytorch framework and the baseline code.
Aug 16, 2019
Market | R@1 | R@5 | R@10 | mAP | Reference |
---|---|---|---|---|---|
IDE+ERA | 89.9% | 96.4% | 97.6% | 75.6% | train_ide.py |
IDE+MHN6 | 93.1% | 97.7% | 98.7% | 83.2% | train_ide.py |
PCB+ERA | 91.7% | 97.4% | 98.3% | 76.4% | train_smallPCB |
PCB+MHN4 | 94.3% | 98.0% | 98.8% | 83.9% | train_smallPCB |
PCB+MHN6 | 94.8% | 98.3% | 98.9% | 85.2% | train_smallPCB_multiGPU.py |
Duke | R@1 | R@5 | R@10 | mAP | Reference |
---|---|---|---|---|---|
IDE+ERA | 82.7% | 91.8% | 94.1% | 68.1% | train_ide.py |
IDE+MHN6 | 87.8% | 94.2% | 95.8% | 74.6% | train_ide.py |
PCB+ERA | 82.9% | 91.7% | 93.8% | 67.7% | train_smallPCB |
PCB+MHN4 | 88.5% | 94.5% | 96.1% | 76.9% | train_smallPCB |
PCB+MHN6 | 89.5% | 94.7% | 96.1% | 77.5% | train_smallPCB_multiGPU.py |
train_ide.py test_ide.py
train_smallPCB.py test_smallPCB.py
train_smallPCB_multiGPU.py test_smallPCB.py
auto_test.sh
Pytorch(0.4.0+)
python3.6
2GPUs, each > 11G
python3 train_ide.py --gpu_ids 0 --name ide --data_dir datasets/Market/datasets/pytorch/ --train_all --batchsize 32 --erasing_p 0.4 --balance_sampler
python3 train_ide.py --gpu_ids 0 --name ide_mhn6 --data_dir datasets/Market/datasets/pytorch/ --train_all --batchsize 32 --erasing_p 0.4 --balance_sampler --alpha 1.4 --parts 6 --mhn
python3 train_smallPCB.py --gpu_ids 0 --name pcb --data_dir datasets/Market/datasets/pytorch/ --train_all --batchsize 32 --erasing_p 0.4 --balance_sampler
python3 train_smallPCB.py --gpu_ids 0 --name pcb_mhn4 --data_dir datasets/Market/datasets/pytorch/ --train_all --batchsize 32 --erasing_p 0.4 --balance_sampler --alpha 2 --parts 4 --mhn
python3 train_smallPCB_multiGPU.py --gpu_ids 0,1 --name pcb_mhn6 --data_dir datasets/Market/datasets/pytorch/ --train_all --batchsize 32 --erasing_p 0.4 --balance_sampler --alpha 2 --parts 6 --mhn
the trained models are stored in folder "model/($name)".
We provide the auto-testing code in auto_test.sh, you can replace the corresponding code for testing. For example,
python3 test_ide.py --gpu_ids $gpu_ids --name ide --test_dir datasets/Market/datasets/pytorch/ --batchsize 32 --which_epoch $i
python3 test_ide.py --gpu_ids $gpu_ids --name ide_mhn6 --test_dir datasets/Market/datasets/pytorch/ --batchsize 20 --which_epoch $i --mhn --parts 6
python3 test_smallPCB.py --gpu_ids $gpu_ids --name pcb --test_dir datasets/Market/datasets/pytorch/ --batchsize 32 --which_epoch $i
python3 test_smallPCB.py --gpu_ids $gpu_ids --name pcb_mhn4 --test_dir datasets/Market/datasets/pytorch/ --batchsize 15 --which_epoch $i --mhn --parts 4
python3 test_smallPCB.py --gpu_ids $gpu_ids --name pcb_mhn6 --test_dir datasets/Market/datasets/pytorch/ --batchsize 10 --which_epoch $i --mhn --parts 6
You are encouraged to cite the following papers if this work helps your research.
@inproceedings{chen2019mixed,
title={Mixed High-Order Attention Network for Person Re-Identification},
author={Chen, Binghui and Deng, Weihong and Hu, Jiani},
booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
year={2019},
}
@InProceedings{chen2019energy,
author = {Chen, Binghui and Deng, Weihong},
title = {Energy Confused Adversarial Metric Learning for Zero-Shot Image Retrieval and Clustering},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019}
}
Copyright (c) Binghui Chen
All rights reserved.
MIT License
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.