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# Changelog | ||
# 变更日志 | ||
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## MMSelfSup | ||
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### v0.4.0 (13/12/2021) | ||
### v0.5.0 (16/12/2021) | ||
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#### Highlight | ||
* Released with code refactor. | ||
* Add 3 new self-supervised learning algorithms. | ||
* Support benchmarks with MMDet and MMSeg. | ||
* Add comprehensive documents. | ||
#### 亮点 | ||
* 代码重构后发版。 | ||
* 添加 3 个新的自监督学习算法。 | ||
* 支持 MMDet 和 MMSeg 的基准测试。 | ||
* 添加全面的文档。 | ||
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#### Refactor | ||
* Merge redundant dataset files. | ||
* Adapt to new version of MMCV and remove old version related codes. | ||
* Inherit MMCV BaseModule. | ||
* Optimize directory. | ||
* Rename all config files. | ||
#### 重构 | ||
* 合并冗余数据集文件。 | ||
* 适配新版 MMCV,去除旧版相关代码。 | ||
* 继承 MMCV BaseModule。 | ||
* 优化目录结构。 | ||
* 重命名所有配置文件。 | ||
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#### New Features | ||
* Add SwAV, SimSiam, DenseCL algorithm. | ||
* Add tsne visualization tools. | ||
* Support MMCV version fp16. | ||
#### 新特性 | ||
* 添加 SwAV、SimSiam、DenseCL 算法。 | ||
* 添加 t-SNE 可视化工具。 | ||
* 支持 MMCV 版本 fp16。 | ||
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#### Benchmarks | ||
* More benchmarking results, including classification, detection and segmentation. | ||
* Support some new datasets in downstream tasks. | ||
* Launch MMDet and MMSeg training with MIM. | ||
#### 基准 | ||
* 更多基准测试结果,包括分类、检测和分割。 | ||
* 支持下游任务中的一些新数据集。 | ||
* 使用 MIM 启动 MMDet 和 MMSeg 训练。 | ||
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#### Docs | ||
* Refactor README, getting_started, install, model_zoo files. | ||
* Add data_prepare file. | ||
* Add comprehensive tutorials. | ||
#### 文档 | ||
* 重构 README、getting_started、install、model_zoo 文档。 | ||
* 添加数据准备文档。 | ||
* 添加全面的教程。 | ||
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## OpenSelfSup (History) | ||
## OpenSelfSup (历史) | ||
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### v0.3.0 (14/10/2020) | ||
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#### Highlight | ||
* Support Mixed Precision Training | ||
* Improvement of GaussianBlur doubles the training speed | ||
* More benchmarking results | ||
#### 亮点 | ||
* 支持混合精度训练。 | ||
* 改进 GaussianBlur 使训练速度加倍。 | ||
* 更多基准测试结果。 | ||
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#### Bug Fixes | ||
* Fix bugs in moco v2, now the results are reproducible. | ||
* Fix bugs in byol. | ||
#### Bug 修复 | ||
* 修复 moco v2 中的 bugs,现在结果可复现。 | ||
* 修复 byol 中的 bugs。 | ||
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#### New Features | ||
* Mixed Precision Training | ||
* Improvement of GaussianBlur doubles the training speed of MoCo V2, SimCLR, BYOL | ||
* More benchmarking results, including Places, VOC, COCO | ||
#### 新特性 | ||
* 混合精度训练。 | ||
* 改进 GaussianBlur 使 MoCo V2、SimCLR、BYOL 的训练速度加倍。 | ||
* 更多基准测试结果,包括 Places、VOC、COCO。 | ||
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### v0.2.0 (26/6/2020) | ||
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#### Highlights | ||
* Support BYOL | ||
* Support semi-supervised benchmarks | ||
#### 亮点 | ||
* 支持 BYOL。 | ||
* 支持半监督基准测试。 | ||
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#### Bug Fixes | ||
* Fix hash id in publish_model.py | ||
#### Bug 修复 | ||
* 修复 publish_model.py 中的哈希 id。 | ||
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#### New Features | ||
#### 新特性 | ||
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* Support BYOL. | ||
* Separate train and test scripts in linear/semi evaluation. | ||
* Support semi-supevised benchmarks: benchmarks/dist_train_semi.sh. | ||
* Move benchmarks related configs into configs/benchmarks/. | ||
* Provide benchmarking results and model download links. | ||
* Support updating network every several iterations. | ||
* Support LARS optimizer with nesterov. | ||
* Support excluding specific parameters from LARS adaptation and weight decay required in SimCLR and BYOL. | ||
* 支持 BYOL。 | ||
* 在线性和半监督评估中将训练和测试脚本分开。 | ||
* 支持半监督基准测试:benchmarks/dist_train_semi.sh。 | ||
* 将基准测试相关的配置文件移动到 configs/benchmarks/。 | ||
* 提供基准测试结果和模型下载链接。 | ||
* 支持每隔几次迭代更新网络。 | ||
* 支持带有 Nesterov 的 LARS 优化器。 | ||
* 支持 SimCLR 和 BYOL 从 LARS 适应和权重衰减中排除特定参数的需求。 |