From fc56a6987bd9ebdd4848026b02ec734135d27aca Mon Sep 17 00:00:00 2001 From: ceci3 Date: Thu, 10 Jun 2021 12:33:28 +0800 Subject: [PATCH] fix dead link (#798) --- README.md | 2 +- README_en.md | 24 ++++++++++++------------ demo/detection/README.md | 2 +- docs/zh_cn/model_zoo.md | 2 +- 4 files changed, 15 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index 65eb7933..fd13143f 100755 --- a/README.md +++ b/README.md @@ -47,7 +47,7 @@ PaddleSlim支持以下功能，也支持自定义量化、裁剪等功能。 Quantization Pruning NAS - Distilling + Distilling diff --git a/README_en.md b/README_en.md index 0c46a65f..18d2cbc9 100755 --- a/README_en.md +++ b/README_en.md @@ -96,7 +96,7 @@ pip install paddleslim==1.2.0 -i https://pypi.tuna.tsinghua.edu.cn/simple - [Algorithm Background](https://paddleslim.readthedocs.io/en/latest/intro_en.html): Introduce the background of quantization, pruning, distillation, NAS. -- [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/master/slim): Introduce how to use PaddleSlim in PaddleDetection library. +- [PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/slim): Introduce how to use PaddleSlim in PaddleDetection library. - [PaddleSeg](https://github.com/PaddlePaddle/PaddleSeg/tree/develop/slim): Introduce how to use PaddleSlim in PaddleSeg library. @@ -112,9 +112,9 @@ Dataset: ImageNet2012; Model: MobileNetV1; |Method |Accuracy(baseline: 70.91%) |Model Size(baseline: 17.0M)| |:---:|:---:|:---:| -| Knowledge Distillation(ResNet50)| [+1.06%]() |-| -| Knowledge Distillation(ResNet50) + int8 quantization |[+1.10%]()| [-71.76%]()| -| Pruning(FLOPs-50%) + int8 quantization|[-1.71%]()|[-86.47%]()| +| Knowledge Distillation(ResNet50)| +1.06% |-| +| Knowledge Distillation(ResNet50) + int8 quantization |+1.10%| -71.76%| +| Pruning(FLOPs-50%) + int8 quantization|-1.71%|-86.47%| ### Object Detection @@ -123,17 +123,17 @@ Dataset: ImageNet2012; Model: MobileNetV1; | Method | mAP(baseline: 76.2%) | Model Size(baseline: 94MB) | | :---------------------: | :------------: | :------------:| -| Knowledge Distillation(ResNet34-YOLOv3) | [+2.8%]() | - | -| Pruning(FLOPs -52.88%) | [+1.4%]() | [-67.76%]() | -|Knowledge DistillationResNet34-YOLOv3)+Pruning(FLOPs-69.57%)| [+2.6%]()|[-67.00%]()| +| Knowledge Distillation(ResNet34-YOLOv3) | +2.8% | - | +| Pruning(FLOPs -52.88%) | +1.4% | -67.76% | +|Knowledge DistillationResNet34-YOLOv3)+Pruning(FLOPs-69.57%)| +2.6%|-67.00%| #### Dataset: COCO; Model: MobileNet-V1-YOLOv3 | Method | mAP(baseline: 29.3%) | Model Size| | :---------------------: | :------------: | :------:| -| Knowledge Distillation(ResNet34-YOLOv3) | [+2.1%]() |-| -| Knowledge Distillation(ResNet34-YOLOv3)+Pruning(FLOPs-67.56%) | [-0.3%]() | [-66.90%]()| +| Knowledge Distillation(ResNet34-YOLOv3) | +2.1% |-| +| Knowledge Distillation(ResNet34-YOLOv3)+Pruning(FLOPs-67.56%) | -0.3% | -66.90%| ### NAS @@ -141,6 +141,6 @@ Dataset: ImageNet2012; Model: MobileNetV2 |Device | Infer time cost | Top1 accuracy(baseline:71.90%) | |:---------------:|:---------:|:--------------------:| -| RK3288 | [-23%]() | +0.07% | -| Android cellphone | [-20%]() | +0.16% | -| iPhone 6s | [-17%]() | +0.32% | +| RK3288 | -23% | +0.07% | +| Android cellphone | -20% | +0.16% | +| iPhone 6s | -17% | +0.32% | diff --git a/demo/detection/README.md b/demo/detection/README.md index a4885306..32160d63 100644 --- a/demo/detection/README.md +++ b/demo/detection/README.md @@ -83,7 +83,7 @@ ### 蒸馏通道剪裁模型 -可通过高精度模型蒸馏通道剪裁后模型的方式，训练方法及相关示例见[蒸馏通道剪裁模型](https://github.com/PaddlePaddle/PaddleDetection/blob/master/slim/extensions/distill_pruned_model/distill_pruned_model_demo.ipynb)。 +可通过高精度模型蒸馏通道剪裁后模型的方式，训练方法及相关示例见[蒸馏通道剪裁模型](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/static/slim/extensions/distill_pruned_model/distill_pruned_model_demo.ipynb)。 COCO数据集上蒸馏通道剪裁模型库如下。 diff --git a/docs/zh_cn/model_zoo.md b/docs/zh_cn/model_zoo.md index a2fa07cb..b8f782f7 100644 --- a/docs/zh_cn/model_zoo.md +++ b/docs/zh_cn/model_zoo.md @@ -199,7 +199,7 @@ PaddleLite版本： v2.3 | BlazeFace-NAS | - | 8 | 640 | 83.7/80.7/65.8 | 244 | 21.117 |[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) | | BlazeFace-NASV2 | SANAS | 8 | 640 | 87.0/83.7/68.5 | 389 | 22.558 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) | -Note: 硬件延时时间是利用提供的硬件延时表得到的，硬件延时表是在855芯片上基于PaddleLite测试的结果。BlazeFace-NASV2的详细配置在[这里](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/face_detection/blazeface_nas_v2.yml). +Note: 硬件延时时间是利用提供的硬件延时表得到的，硬件延时表是在855芯片上基于PaddleLite测试的结果。BlazeFace-NASV2的详细配置在[这里](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/face_detection/blazeface_nas_v2.yml)。 ## 3. 图像分割 -- GitLab