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META
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ECCV 2022
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META
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# META [ECCV 2022] Mimic Embedding via Adaptive Aggregation: Learning Generalizable Person Re-identification [[paper]](https://arxiv.org/pdf/2112.08684.pdf) ### Update 2022-07-14: Update Code. 2022-08-03: Update Paper. ### Datasets * Requirements: Market1501, CUHK03, CUHK-SYSU, MSMT17_v2 Please put all the datasets in one directory, and modify DATASETS in ```projects/META/configs/Base-cnn.yml``` ### Installation * Please check [fast-reid](https://github.com/JDAI-CV/fast-reid/blob/master/INSTALL.md) for fast-reid installation * Please check [Apex](https://github.com/NVIDIA/apex) for Apex installation * Compile with cython to accelerate evalution: ``` bash cd fastreid/evaluation/rank_cylib; make all ``` ### Train If you want to train with 1-GPU, run: ``` python projects/META/train_net.py --config-file projects/META/configs/r50.yml MODEL.DEVICE "cuda:0" ``` If you want to train with 4-GPU, run: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 python projects/META/train_net.py --config-file projects/META/configs/r50.yml --num-gpus 4 ``` You can get the results in our paper by training with 4-GPU, please modify SOLVER.IMS_PER_BATCH in ```projects/META/configs/Base-cnn.yml``` (64 for 1-GPU and 256 for 4-GPU) ### Evaluation To evaluate a model's performance, use: ``` python projects/META/train_net.py --config-file projects/META/configs/r50.yml --eval-only MODEL.WEIGHTS /path/to/checkpoint_file MODEL.DEVICE "cuda:0" ``` ## Contacts If you have any question about the project, please feel free to contact me. E-mail: boqiang.xu@cripac.ia.ac.cn ## ACKNOWLEDGEMENTS The code was developed based on the ’fast-reid’ toolbox https://github.com/JDAI-CV/fast-reid.
