From 894f5a5bae8e16457a1822bb793612c5b603d709 Mon Sep 17 00:00:00 2001 From: cuicheng01 Date: Fri, 17 Sep 2021 03:38:01 +0000 Subject: [PATCH] Update README --- README_ch.md | 2 +- README_en.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README_ch.md b/README_ch.md index e0f613a3..74b9c131 100644 --- a/README_ch.md +++ b/README_ch.md @@ -7,7 +7,7 @@ 飞桨图像识别套件PaddleClas是飞桨为工业界和学术界所准备的一个图像识别任务的工具集，助力使用者训练出更好的视觉模型和应用落地。 **近期更新** -- 2021.09.08 增加PaddleClas自研PPLCNet系列模型, 这些模型在Intel CPU上有较强的竞争力。相关指标和预训练权重可以从 [这里](docs/zh_CN/ImageNet_models.md)下载。 +- 2021.09.17 增加PaddleClas自研PP-LCNet系列模型, 这些模型在Intel CPU上有较强的竞争力。相关指标和预训练权重可以从 [这里](docs/zh_CN/ImageNet_models.md)下载。 - 2021.08.11 更新7个[FAQ](docs/zh_CN/faq_series/faq_2021_s2.md)。 - 2021.06.29 添加Swin-transformer系列模型，ImageNet1k数据集上Top1 acc最高精度可达87.2%；支持训练预测评估与whl包部署，预训练模型可以从[这里](docs/zh_CN/models/models_intro.md)下载。 - 2021.06.22,23,24 PaddleClas官方研发团队带来技术深入解读三日直播课。课程回放：[https://aistudio.baidu.com/aistudio/course/introduce/24519](https://aistudio.baidu.com/aistudio/course/introduce/24519) diff --git a/README_en.md b/README_en.md index 2436554e..aa65ce38 100644 --- a/README_en.md +++ b/README_en.md @@ -8,7 +8,7 @@ PaddleClas is an image recognition toolset for industry and academia, helping us **Recent updates** -- 2021.09.08 Add PPLCNet series model developed by PaddleClas, these models show strong competitiveness on Intel CPUs. The metrics and pretrained model are available [here](docs/en/ImageNet_models_en.md). +- 2021.09.17 Add PP-LCNet series model developed by PaddleClas, these models show strong competitiveness on Intel CPUs. The metrics and pretrained model are available [here](docs/en/ImageNet_models_en.md). - 2021.06.29 Add Swin-transformer series model，Highest top1 acc on ImageNet1k dataset reaches 87.2%, training, evaluation and inference are all supported. Pretrained models can be downloaded [here](docs/en/models/models_intro_en.md). - 2021.06.16 PaddleClas release/2.2. Add metric learning and vector search modules. Add product recognition, animation character recognition, vehicle recognition and logo recognition. Added 30 pretrained models of LeViT, Twins, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper. -- GitLab