mirrors
/
fast-reid
mirror of https://github.com/JDAI-CV/fast-reid.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity
210 Commits
1 Branch
4 Tags
15 MiB
Commit Graph

26 Commits (56a1ab4a5d568035af8ae005e82aedcafd5b78fa)

Author SHA1 Message Date
liaoxingyu 56a1ab4a5d update fast global avgpool 
Summary: update fast pool according to https://arxiv.org/pdf/2003.13630.pdf
 2020-06-12 16:34:03 +08:00
liaoxingyu cbdc01a1c3 update pairwise circle loss 
Summary: add param of pairwise circle loss to config, and update pairwise circle loss version
 2020-06-10 19:07:29 +08:00
liaoxingyu 84c733fa85 fix: remove prefetcher, put normalizer in model 
1. remove messy data prefetcher which will cause  confusion
2. put normliazer in model to accelerate training via GPU computing
 2020-05-25 23:39:11 +08:00
liaoxingyu fd90555e19 feat: add multi-dataset joint training 
new feature that can support joint training, and find some bugs in funtion combine_all of datasets/bases.py that assume person id in dataset has been relabeld from 0 to num_class.
Another bug appears in msmt17 which trainset and testset person id both begin from 0, and we should change testset id from num_class of trainset.
 2020-05-18 20:06:04 +08:00
liaoxingyu bf18479541 fix: revise syncBN bug 2020-05-14 14:52:37 +08:00
liaoxingyu 5ae3d4fecf feat: add aqe support in test phase 
query expansion will combine the retrived topk nearest neighbors with the original query feature,
it will enhance mAP by a large margin.feat:
 2020-05-13 16:27:22 +08:00
liaoxingyu 320010f2ae feat: support re-rank in test phase 2020-05-13 11:47:52 +08:00
liaoxingyu 0b15ac4e03 feat(hooks&optim): update stochastic weight averging hooks 
Update swa method which will do after regular training if you
set this option enabled.
 2020-05-08 12:20:04 +08:00
liaoxingyu 948af64fd1 feat: add swa algorithm 
Add swa and related config options,
if it is enabled, model will do swa after regular training
 2020-05-06 10:17:44 +08:00
liaoxingyu a2dcd7b4ab feat(layers/norm): add ghost batchnorm 
add a get_norm fucntion to easily change normalization between batchnorm, ghost bn and group bn
 2020-05-01 09:02:46 +08:00
liaoxingyu a6bd0371e2 feat($data): add autoaugment 
add auto augmentation support with ImageNet policy and CIFAR10 policy.
modify codes in transforms and config for adapting to this augmentation.
 2020-04-27 11:41:12 +08:00
liaoxingyu 3984f0c91d refactor($modeling/meta): refactor heads output 
without intermediate variables generated by reid heads, make it more flexible
 2020-04-24 12:16:18 +08:00
liaoxingyu 95a3c62ad2 refactor(fastreid) 
refactor architecture
 2020-04-20 10:59:29 +08:00
liaoxingyu 9684500a57 chagne arch 
1. change dataset show to trainset show and testset show seperately
2. add cls layer to easily plug in circle loss and arcface
 2020-04-19 12:54:01 +08:00
liaoxingyu be9faa5605 update focal loss 
update dataset info display
update seperate lr
update adaptive label smooth regularization
 2020-04-17 13:46:10 +08:00
liaoxingyu 4d2fa28dbb update freeze layer 
update preciseBN
update circle loss with metric learning and cross entropy loss form
update loss call methods
 2020-04-06 23:34:27 +08:00
liaoxingyu 6a8961ce48 1. upload circle loss and arcface 
2. finish freeze training
3. update augmix data augmentation
 2020-04-05 23:54:26 +08:00
liaoxingyu eacee874aa fix merge 2020-03-25 11:06:39 +08:00
liaoxingyu 23bedfce12 update version0.2 code 2020-03-25 10:58:26 +08:00
L1aoXingyu 12957f66aa Change architecture: 
1. delete redundant preprocess
2. add data prefetcher to accelerate data loading
3. fix minor bug of triplet sampler when only one image for one id
 2020-02-18 21:01:23 +08:00
L1aoXingyu 327d74ffbb Update strong baseline result 
Change data sampler
 2020-02-13 00:19:15 +08:00
L1aoXingyu a2f69d0537 Update StrongBaseline results for market1501 and dukemtmc 2020-02-11 22:38:40 +08:00
L1aoXingyu 8a9c0ccfad Finish first version for fastreid 2020-02-10 22:13:04 +08:00
L1aoXingyu db6ed12b14 Update sampler code 2020-02-10 07:38:56 +08:00
liaoxingyu 71950d2c09 1. Fix evaluation code 
2. Finish multi-dataset evaluation
3. Decouple image preprocess and output postprocess with model forward for DataParallel training
4. Finish build backbone registry
5. Fix dataset sampler
 2020-01-21 20:24:26 +08:00
liaoxingyu b761b656f3 Finish basic training loop and evaluation results 2020-01-20 21:33:37 +08:00