Releases: open-mmlab/mmselfsup
MMSelfSup Release v1.0.0
MMSelfSup
v1.0.0 (06/04/2023)
Highlight
- Support
PixMIM
. - Support
DINO
inprojects/dino/
.
New Features
- Support
PixMIM
(#721) - Support
DINO
inprojects/dino/
(#658) - Support auto import modules from registry (#660)
Bug Fixes
- Fix registry import error of MMDet (#732)
- Fix local-rank in pytorch2.0 (#728)
- Update MAE 300e pt results (#722)
- Add missing data preprocessor in tsne configs (#715)
- Fix the bug in shape bias (#717)
- Fix T-SNE TypeError (#708)
Improvements
Docs
- Update doc links (#738)
- Translate customize_runtime.md (#734)
- Add media links and mmpretrain announcement (#730, #693)
- Translate two docs (#725)
- Translate docs (#723)
Contributors
A total of 9 developers contributed to this release.
Thanks @fangyixiao18 , @tonysy , @YuanLiuuuuuu , @vansin , @guneetmutreja , @SheffieldCao , @howayShi , @xuan07472 , @jts250
New Contributors
- @vansin made their first contribution in #693
- @guneetmutreja made their first contribution in #708
- @SheffieldCao made their first contribution in #723
- @howayShi made their first contribution in #658
- @xuan07472 made their first contribution in #725
- @jts250 made their first contribution in #734
Full Changelog: v1.0.0rc6...v1.0.0
MMSelfSup Release v1.0.0rc6
v1.0.0rc6 (10/02/2023)
Highlight
- Support
MaskFeat
with video dataset inprojects/maskfeat_video/
- Translate documentation to Chinese.
New Features
- Support
MaskFeat
with video dataset inprojects/maskfeat_video/
(#678)
Bug Fixes
- Fix distributed setting for shape bias (#689)
- Update link of beitv2 (#676)
- Pass param by explicitly setting location (#654)
- Update default_runtime.py (#681)
- Rename metafile.yaml to metafile.yml (#680)
- Fix bugs in configs/selfsup/eva/metafile.yaml (#669)
Improvements
- Switch default branch to 1.x (#686)
- Update pre-commit (#685)
- Deprecate the support of python 3.6 (#657)
Docs
- Translate add_transforms.md and conventions.md (#674)
- Translate classification.md, detection.md, segmentation.md (#665)
- Update link of knn script (#661)
- Translate two docs (#653)
- Translate three docs (#651)
Contributors
A total of 7 developers contributed to this release.
Thanks @fangyixiao18 , @YuanLiuuuuuu , @GeoffreyChen777 , @okotaku , @Caczhtus , @123456789asdfjkl , @JetstramSam
New Contributors
- @JetstramSam made their first contribution in #651
- @123456789asdfjkl made their first contribution in #665
- @Caczhtus made their first contribution in #674
- @okotaku made their first contribution in #680
- @GeoffreyChen777 made their first contribution in #681
Full Changelog: v1.0.0rc5...v1.0.0rc6
MMSelfSup Release v1.0.0rc5
v1.0.0rc5 (30/12/2022)
Highlight
- Support
BEiT v2
,MixMIM
,EVA
- Support
ShapeBias
for model analysis - Add Solution of FGIA ACCV 2022 (1st Place)
- Refactor t-SNE
New Features
- Support
BEiT v2
(#627) - Support
MixMIM
(#626) - Support
EVA
(#632) - Support
ShapeBias
for model analysis (#635) - Add convert scripts and instructions on seg and det (#621)
- Add pretraining for FGIA (#607)
Bug Fixes
- Change
pseudo_collect
todefault_collect
(#616) - Fix the link of SimMIM 800pt 100ft (#622)
- Change
map_location
tocpu
(#623) - Fix import error (#631)
- Fix key error in configs (#630)
- Change
np.int
toint
(#636) - Fix knn multi-gpu bug (#634)
Improvements
- Refactor
projects/
folder (#620) - Refactor
t-SNE
(#629) - Refactor
CAE
(#645) - Refactor benchmark script and update files (#637)
Docs
Contributors
A total of 6 developers contributed to this release.
Thanks @fangyixiao18 , @YuanLiuuuuuu , @tonysy , @wangbo-zhao , @WasedaMagina , @ZhaoQiiii
MMSelfSup Release v0.11.0
v0.11.0 (30/12/2022)
New Features
- Support InterCLR (#609)
Bug Fixes
- Fix potential bug of hook registration (#647)
- Fix sampling_replace config kwargs bug (#646)
- Change sklearn to scikit-learn in requirements (#583)
Improvements
Docs
- Add global notes and the version switcher menu (#573)
Contributors
A total of 3 developers contributed to this release.
Thanks @fangyixiao18 , @Jiahao000 , @bencwallace
MMSelfSup Release v1.0.0rc4
v1.0.0rc4 (07/12/2022)
The master
branch is still 0.x version and we will checkout a new 1.x
branch to release 1.x version. The two versions will be maintained simultaneously in the future.
We briefly list the major breaking changes here. Please refer to the migration guide for details and migration instructions.
Highlight
- Support
BEiT
andMILAN
- Support low-level reconstruction visualization
New Features
Bug Fixes
Improvements
Docs
- Update readthedocs rst and menu button (#572)
- Add readthedocs algorithm pages and fix some displaying error (#599)
Contibutors
A total of 3 developers contributed to this release.
Thanks @fangyixiao18 , @YuanLiuuuuuu , @soonera
MMSelfSup Release v1.0.0rc3
v1.0.0rc3
The master
branch is still 0.x version and we will checkout a new 1.x
branch to release 1.x version. The two versions will be maintained simultaneously in the future.
We briefly list the major breaking changes here. Please refer to the migration guide for details and migration instructions.
Highlight
- Support
MaskFeat
New Features
Bug Fixes
- Fix fine-tuning config of MAE-H-448 (#509)
Improvements
Docs
- Add custom dataset tutorial (#522)
- Refactor add_modules.md (#524)
- Translate some documentation to Chinese
Contributors
A total of 6 developers contributed to this release.
Thanks @fangyixiao18 , @YuanLiuuuuuu , @tonysy , @Jiahao000 , @wangbo-zhao , @soonera
MMSelfSup Release v0.10.1
v0.10.1 (01/11/2022)
Improvements
Docs
Contributor
A total of 3 developers contributed to this release.
Thanks @fangyixiao18 , @isLinXu , @nijkah
MMSelfSup Release v1.0.0rc2
v1.0.0rc2 (12/10/2022)
The master
branch is still 0.x version and we will checkout a new 1.x
branch to release 1.x version. The two versions will be maintained simultaneously in the future.
We briefly list the major breaking changes here. Please refer to the migration guide for details and migration instructions.
Highlight
- Full support of
MAE
,SimMIM
,MoCoV3
.
New Features
Bug Fixes
Improvements
- Refactor colab tutorial (#470))
- Update readthedocs requirements (#472)
- Update CI (#476)
- Refine
mim_slurm_test.sh
andmim_dist_test.sh
for benchmarks (#477) - Update Metafile format and content (#478)
Docs
- Add advanced_guides/engine.md (#454)
- Add advanced_guides/evaluation.md (#456)
- add advanced_guides/transforms.md (#463)
- Add dataset docs (#437)
- Refine contribution guide (#492)
- update convention (#475)
Contributors
MMSelfSup Release v0.10.0
v0.10.0 (30/09/2022)
Highlight
New Features
- Support MaskFeat (#485)
Bug Fixes
Improvements
- Change hook_cfg type access (#409)
- Support to dump training config (#410)
- Support to save MAE visualization results (#388)
- Remove default value of deprecated option (#490)
Docs
- Update the link of MAE (#497)
- Update README to announce 1.0.0rc version (#474)
- Update get_started.md (#402)
- Update model zoo (#499)
Contributors
A total of 8 developers contributed to this release.
Thanks @fangyixiao18 , @YuanLiuuuuuu , @soonera , @Jason-Study , @daidaiershidi , @lorinczszabolcs , @jingt2ch , @Happylkx
MMSelfSup Release v1.0.0rc1
We are excited to announce the release of MMSelfSup v1.0.0rc1.
MMSelfSup v1.0.0rc1 is the first version of MMSelfSup 1.x, a part of the OpenMMLab 2.0 projects.
The master
branch is still 0.x version and we will checkout a new 1.x
branch to release 1.x version. The two versions will be maintained simultaneously in the future.
We briefly list the major breaking changes here. Please refer to the migration guide for details and migration instructions.
Highlight
- Based on MMEngine and MMCV.
- Released with refactor.
- Datasets
- Models
- Config
- ...
- Refine all documents.
New Features
- Add
SelfSupDataSample
to unify the components' interface. - Add
SelfSupVisualizer
for visualization. - Add
SelfSupDataPreprocessor
for data preprocess in model.
Improvements
- Most algorithms now support non-distributed training.
- Change the interface of different data augmentation transforms to
dict
. - Run classification downstream task with MMClassification.
Docs
- Refine all documents and reorganize the directory.
- Add concepts for different components.