EANet
EANet: Enhancing Alignment for Cross-Domain Person Re-identification
파일 탐색기
최종 버전 다운로드 (.zip)- dataset_market1501_Market-1501-v15.09.15_query_0051_c2s1_006051_00.jpg
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- dataset_market1501_Market-1501-v15.09.15_query_0051_c2s1_006051_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_0058_c3s1_006826_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_0096_c1s1_015851_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_0157_c5s1_026726_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_0422_c5s3_081437_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_0455_c5s1_116245_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_0501_c1s2_064721_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_0514_c3s1_141483_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_0568_c1s3_019626_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_0602_c6s2_010968_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_0617_c2s2_016562_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_1180_c5s3_006593_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_1256_c1s5_047166_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_1270_c4s5_052385_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_1302_c6s4_019777_00.jpg
- dataset_market1501_Market-1501-v15.09.15_query_1497_c1s6_013421_00.jpg
- model.png
- __init__.py
- default.py
- __init__.py
- coco.py
- cuhk03_np_detected_jpg.py
- cuhk03_np_detected_png.py
- duke.py
- market1501.py
- msmt17.py
- partial_ilids.py
- partial_reid.py
- __init__.py
- create_dataset.py
- dataloader.py
- dataset.py
- kpt_to_pap_mask.py
- multitask_dataloader.py
- random_identity_sampler.py
- transform.py
- __init__.py
- eval_dataloader.py
- eval_feat.py
- extract_feat.py
- metric.py
- np_distance.py
- re_ranking.py
- torch_distance.py
- __init__.py
- id_loss.py
- loss.py
- ps_loss.py
- triplet_loss.py
- __init__.py
- backbone.py
- base_model.py
- global_pool.py
- model.py
- pa_pool.py
- pcb_pool.py
- ps_head.py
- resnet.py
- __init__.py
- cft_trainer.py
- eanet_trainer.py
- lr_scheduler.py
- optimizer.py
- reid_trainer.py
- trainer.py
- __init__.py
- arg_parser.py
- cfg.py
- file.py
- image.py
- init_path.py
- log.py
- meter.py
- misc.py
- model.py
- rank_list.py
- torch_utils.py
- __init__.py
- GlobalPool.txt
- PAP.txt
- PAP_6P.txt
- PAP_S_PS.txt
- PAP_S_PS_Triplet_Loss_Market1501.txt
- PAP_ST_PS.txt
- PAP_ST_PS_SPGAN.txt
- PAP_ST_PS_SPGAN_CFT.txt
- PAP_StC_PS.txt
- PCB.txt
- infer_dataloader_example.py
- remove_optim_lr_s_in_ckpt.py
- test_all.sh
- test_PAP_S_PS_reranking.sh
- train.sh
- train_all.sh
- train_PAP_S_PS_Triplet_Loss_Market1501.sh
- visualize_rank_list.py
- .gitignore
- README.md
- requirements.txt
# 설치 가이드
1. 코드 내려받기
git clone https://github.com/huanghoujing/EANet
깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.
cd EANet
방금 내려받은 프로젝트 폴더 안으로 이동합니다.
2. Python
쉬움 추천사전 준비물
CUDA_VISIBLE_DEVICES=0 python -m package.optim.eanet_trainer --exp_dir exp/eanet/GlobalPool/market1501 --cfg_file package/config/default.py --ow_file paper_configs/GlobalPool.txt --ow_str "cfg.dataset.train.name = 'market1501'"
파이썬 스크립트(또는 모듈)를 실행합니다.
CUDA_VISIBLE_DEVICES=0 python -m package.optim.eanet_trainer --exp_dir exp/eanet/GlobalPool/market1501 --cfg_file package/config/default.py --ow_file paper_configs/GlobalPool.txt --ow_str "cfg.dataset.train.name = 'market1501'; cfg.only_test = True"
파이썬 스크립트(또는 모듈)를 실행합니다.
CUDA_VISIBLE_DEVICES=0 python -m package.optim.${trainer} --exp_dir ${exp_dir} --cfg_file ${cfg_file} [--ow_file ${ow_file}] [--ow_str ${ow_str}]
파이썬 스크립트(또는 모듈)를 실행합니다.
에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.
이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.
// repository documentation
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