[cherry-pick]modify links about master, test=document_fix (#3319)

* modify links about master, test=document_fix

* modify develop to release/2.1

* correct links, test=document_fix

configs/ppyolo/README.md

@@ -56,7 +56,7 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
 **Notes:**
 
 - PP-YOLO is trained on COCO train2017 dataset and evaluated on val2017 & test-dev2017 dataset，Box AP<sup>test</sup> is evaluation results of `mAP(IoU=0.5:0.95)`.
-- PP-YOLO used 8 GPUs for training and mini-batch size as 24 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/FAQ.md).
+- PP-YOLO used 8 GPUs for training and mini-batch size as 24 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/FAQ.md).
 - PP-YOLO inference speed is tesed on single Tesla V100 with batch size as 1, CUDA 10.2, CUDNN 7.5.1, TensorRT 5.1.2.2 in TensorRT mode.
 - PP-YOLO FP32 inference speed testing uses inference model exported by `tools/export_model.py` and benchmarked by running `depoly/python/infer.py` with `--run_benchmark`. All testing results do not contains the time cost of data reading and post-processing(NMS), which is same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) in testing method.
 - TensorRT FP16 inference speed testing exclude the time cost of bounding-box decoding(`yolo_box`) part comparing with FP32 testing above, which means that data reading, bounding-box decoding and post-processing(NMS) is excluded(test method same as [YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet) too)
@@ -71,7 +71,7 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
 **Notes:**
 
 - PP-YOLO_MobileNetV3 is trained on COCO train2017 datast and evaluated on val2017 dataset，Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5:0.95)`, Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5)`.
-- PP-YOLO_MobileNetV3 used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/FAQ.md).
+- PP-YOLO_MobileNetV3 used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/FAQ.md).
 - PP-YOLO_MobileNetV3 inference speed is tested on Kirin 990 with 1 thread.
 
 ### PP-YOLO tiny
@@ -84,7 +84,7 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods:
 **Notes:**
 
 - PP-YOLO-tiny is trained on COCO train2017 datast and evaluated on val2017 dataset，Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5:0.95)`, Box AP<sup>val</sup> is evaluation results of `mAP(IoU=0.5)`.
-- PP-YOLO-tiny used 8 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/FAQ.md).
+- PP-YOLO-tiny used 8 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/FAQ.md).
 - PP-YOLO-tiny inference speed is tested on Kirin 990 with 4 threads by arm8
 - we alse provide PP-YOLO-tiny post quant inference model, which can compress model to **1.3MB** with nearly no inference on inference speed and performance
 

configs/ppyolo/README_cn.md

@@ -55,7 +55,7 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
 **注意:**
 
 - PP-YOLO模型使用COCO数据集中train2017作为训练集，使用val2017和test-dev2017作为测试集，Box AP<sup>test</sup>为`mAP(IoU=0.5:0.95)`评估结果。
-- PP-YOLO模型训练过程中使用8 GPUs，每GPU batch size为24进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/FAQ.md)调整学习率和迭代次数。
+- PP-YOLO模型训练过程中使用8 GPUs，每GPU batch size为24进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/FAQ.md)调整学习率和迭代次数。
 - PP-YOLO模型推理速度测试采用单卡V100，batch size=1进行测试，使用CUDA 10.2, CUDNN 7.5.1，TensorRT推理速度测试使用TensorRT 5.1.2.2。
 - PP-YOLO模型FP32的推理速度测试数据为使用`tools/export_model.py`脚本导出模型后，使用`deploy/python/infer.py`脚本中的`--run_benchnark`参数使用Paddle预测库进行推理速度benchmark测试结果, 且测试的均为不包含数据预处理和模型输出后处理(NMS)的数据(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
 - TensorRT FP16的速度测试相比于FP32去除了`yolo_box`(bbox解码)部分耗时，即不包含数据预处理，bbox解码和NMS(与[YOLOv4(AlexyAB)](https://github.com/AlexeyAB/darknet)测试方法一致)。
@@ -68,7 +68,7 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
 | PP-YOLO_MobileNetV3_small    |    4    |      32       |    16MB    |   320    |         17.2         |           33.8          |           21.5         | [下载链接](https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_small_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/configs/ppyolo/ppyolo_mbv3_small_coco.yml)                   |
 
 - PP-YOLO_MobileNetV3 模型使用COCO数据集中train2017作为训练集，使用val2017作为测试集，Box AP<sup>val</sup>为`mAP(IoU=0.5:0.95)`评估结果, Box AP50<sup>val</sup>为`mAP(IoU=0.5)`评估结果。
-- PP-YOLO_MobileNetV3 模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/FAQ.md)调整学习率和迭代次数。
+- PP-YOLO_MobileNetV3 模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/FAQ.md)调整学习率和迭代次数。
 - PP-YOLO_MobileNetV3 模型推理速度测试环境配置为麒麟990芯片单线程。
 
 ### PP-YOLO tiny模型
@@ -79,7 +79,7 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度：
 | PP-YOLO tiny                 |     8      |      32       |   4.2MB    |   **1.3M**     |     416     |         22.7         |          65.4         | [model](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_650e_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/configs/ppyolo/ppyolo_tiny_650e_coco.yml) | [预测模型](https://paddledet.bj.bcebos.com/models/ppyolo_tiny_quant.tar) |
 
 - PP-YOLO-tiny 模型使用COCO数据集中train2017作为训练集，使用val2017作为测试集，Box AP<sup>val</sup>为`mAP(IoU=0.5:0.95)`评估结果, Box AP50<sup>val</sup>为`mAP(IoU=0.5)`评估结果。
-- PP-YOLO-tiny 模型训练过程中使用8GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../docs/FAQ.md)调整学习率和迭代次数。
+- PP-YOLO-tiny 模型训练过程中使用8GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/docs/tutorials/FAQ.md)调整学习率和迭代次数。
 - PP-YOLO-tiny 模型推理速度测试环境配置为麒麟990芯片4线程，arm8架构。
 - 我们也提供的PP-YOLO-tiny的后量化压缩模型，将模型体积压缩到**1.3M**，对精度和预测速度基本无影响
 

static/configs/acfpn/README.md

@@ -13,4 +13,4 @@ Cao J, Chen Q, Guo J, et al. Attention-guided Context Feature Pyramid Network fo
 
 | Backbone                | Type     | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           | Configs |
 | :---------------------- | :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
-| ResNet50-vd-ACFPN         | Faster     |     2     |   1x    |     23.432     |  39.6  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_acfpn_1x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/acfpn/faster_rcnn_r50_vd_acfpn_1x.yml) |
+| ResNet50-vd-ACFPN         | Faster     |     2     |   1x    |     23.432     |  39.6  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_acfpn_1x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/acfpn/faster_rcnn_r50_vd_acfpn_1x.yml) |

static/configs/anchor_free/README.md

@@ -26,14 +26,14 @@
 
 | 网络结构 | 骨干网络 | 图片个数/GPU | 预训练模型 | mAP | FPS  | 模型下载 | 配置文件 |
 |:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:----------:|
-| CornerNet-Squeeze    | Hourglass104 | 14  |    无    | 34.5  | 35.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_hg104.tar)  | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/cornernet_squeeze_hg104.yml) |
-| CornerNet-Squeeze    | ResNet50-vd    | 14  |    [faster\_rcnn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_2x.tar)    | 32.7     | 47.01      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_r50_vd_fpn.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/cornernet_squeeze_r50_vd_fpn.yml) |
-| CornerNet-Squeeze-dcn    | ResNet50-vd    | 14  |    [faster\_rcnn\_dcn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_2x.tar)    | 34.9    | 40.43      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_dcn_r50_vd_fpn.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/cornernet_squeeze_dcn_r50_vd_fpn.yml) |
-| CornerNet-Squeeze-dcn-mixup-cosine*    | ResNet50-vd    | 14  |    [faster\_rcnn\_dcn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_2x.tar)    | 38.2    | 39.70      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_dcn_r50_vd_fpn_mixup_cosine.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/cornernet_squeeze_dcn_r50_vd_fpn_mixup_cosine.yml) |
-| FCOS    | ResNet50    | 2  |    [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar)    | 39.8 | 18.85      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_r50_fpn_1x.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/fcos_r50_fpn_1x.yml) |
-| FCOS+multiscale_train    | ResNet50    | 2  |    [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar)    | 42.0 | 19.05      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_r50_fpn_multiscale_2x.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/fcos_r50_fpn_multiscale_2x.yml) |
-| FCOS+DCN    | ResNet50    | 2  |    [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar)    | 44.4 | 13.66      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_dcn_r50_fpn_1x.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/fcos_dcn_r50_fpn_1x.yml) |
-| TTFNet  |  DarkNet53   |   12    |    [DarkNet53_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar)  | 32.9 |  85.92 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ttfnet_darknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/anchor_free/ttfnet_darknet.yml) |
+| CornerNet-Squeeze    | Hourglass104 | 14  |    无    | 34.5  | 35.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_hg104.tar)  | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/anchor_free/cornernet_squeeze_hg104.yml) |
+| CornerNet-Squeeze    | ResNet50-vd    | 14  |    [faster\_rcnn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_2x.tar)    | 32.7     | 47.01      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_r50_vd_fpn.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/anchor_free/cornernet_squeeze_r50_vd_fpn.yml) |
+| CornerNet-Squeeze-dcn    | ResNet50-vd    | 14  |    [faster\_rcnn\_dcn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_2x.tar)    | 34.9    | 40.43      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_dcn_r50_vd_fpn.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/anchor_free/cornernet_squeeze_dcn_r50_vd_fpn.yml) |
+| CornerNet-Squeeze-dcn-mixup-cosine*    | ResNet50-vd    | 14  |    [faster\_rcnn\_dcn\_r50\_vd\_fpn\_2x](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_2x.tar)    | 38.2    | 39.70      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cornernet_squeeze_dcn_r50_vd_fpn_mixup_cosine.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/anchor_free/cornernet_squeeze_dcn_r50_vd_fpn_mixup_cosine.yml) |
+| FCOS    | ResNet50    | 2  |    [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar)    | 39.8 | 18.85      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_r50_fpn_1x.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/anchor_free/fcos_r50_fpn_1x.yml) |
+| FCOS+multiscale_train    | ResNet50    | 2  |    [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar)    | 42.0 | 19.05      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_r50_fpn_multiscale_2x.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/anchor_free/fcos_r50_fpn_multiscale_2x.yml) |
+| FCOS+DCN    | ResNet50    | 2  |    [ResNet50\_cos\_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar)    | 44.4 | 13.66      | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/fcos_dcn_r50_fpn_1x.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/anchor_free/fcos_dcn_r50_fpn_1x.yml) |
+| TTFNet  |  DarkNet53   |   12    |    [DarkNet53_pretrained](https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar)  | 32.9 |  85.92 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ttfnet_darknet.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/anchor_free/ttfnet_darknet.yml) |
 
 **注意:**
 

static/configs/autoaugment/README.md

@@ -19,5 +19,5 @@
 
 | Backbone                | Type     | AutoAug policy | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           | Configs |
 | :---------------------- | :-------------:| :-------: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
-| ResNet50-vd-FPN         | Faster     |   v1 |  2     |   3x    |     22.800     |  39.9  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_aa_3x.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/autoaugment/faster_rcnn_r50_vd_fpn_aa_3x.yml) |
-| ResNet101-vd-FPN         | Faster     |   v1 |  2     |   3x    |     17.652     |  42.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_aa_3x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/autoaugment/faster_rcnn_r101_vd_fpn_aa_3x.yml) |
+| ResNet50-vd-FPN         | Faster     |   v1 |  2     |   3x    |     22.800     |  39.9  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_aa_3x.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/autoaugment/faster_rcnn_r50_vd_fpn_aa_3x.yml) |
+| ResNet101-vd-FPN         | Faster     |   v1 |  2     |   3x    |     17.652     |  42.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r101_vd_fpn_aa_3x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/autoaugment/faster_rcnn_r101_vd_fpn_aa_3x.yml) |

static/configs/gcnet/README.md

@@ -30,5 +30,5 @@
 
 | Backbone                | Type       |     Context| Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           | Configs |
 | :---------------------- | :-------------: |  :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
-| ResNet50-vd-FPN         | Mask       | GC(c3-c5, r16, add)  |     2     |   2x    |     15.31     |  41.4  |    36.8    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_add_r16_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/gcnet/mask_rcnn_r50_vd_fpn_gcb_add_r16_2x.yml) |
-| ResNet50-vd-FPN         | Mask       | GC(c3-c5, r16, mul)  |     2     |   2x    |     15.35     |  40.7  |    36.1    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_mul_r16_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/gcnet/mask_rcnn_r50_vd_fpn_gcb_mul_r16_2x.yml) |
+| ResNet50-vd-FPN         | Mask       | GC(c3-c5, r16, add)  |     2     |   2x    |     15.31     |  41.4  |    36.8    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_add_r16_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/gcnet/mask_rcnn_r50_vd_fpn_gcb_add_r16_2x.yml) |
+| ResNet50-vd-FPN         | Mask       | GC(c3-c5, r16, mul)  |     2     |   2x    |     15.35     |  40.7  |    36.1    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_mul_r16_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/gcnet/mask_rcnn_r50_vd_fpn_gcb_mul_r16_2x.yml) |

static/configs/gcnet/README_cn.md

@@ -42,8 +42,8 @@ SNL模块可以抽象为上下文建模、特征转换和特征聚合三个部
 
 | 骨架网络             | 网络类型 | Context设置     | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | Mask AP |                           下载                          | 配置文件 |
 | :---------------------- | :-------------: |  :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
-| ResNet50-vd-FPN         | Mask       | GC(c3-c5, r16, add)  |     2     |   2x    |     15.31     |  41.4  |    36.8    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_add_r16_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/gcnet/mask_rcnn_r50_vd_fpn_gcb_add_r16_2x.yml) |
-| ResNet50-vd-FPN         | Mask       | GC(c3-c5, r16, mul)  |     2     |   2x    |     15.35     |  40.7  |    36.1    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_mul_r16_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/gcnet/mask_rcnn_r50_vd_fpn_gcb_mul_r16_2x.yml) |
+| ResNet50-vd-FPN         | Mask       | GC(c3-c5, r16, add)  |     2     |   2x    |     15.31     |  41.4  |    36.8    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_add_r16_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/gcnet/mask_rcnn_r50_vd_fpn_gcb_add_r16_2x.yml) |
+| ResNet50-vd-FPN         | Mask       | GC(c3-c5, r16, mul)  |     2     |   2x    |     15.35     |  40.7  |    36.1    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_r50_vd_fpn_gcb_mul_r16_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/gcnet/mask_rcnn_r50_vd_fpn_gcb_mul_r16_2x.yml) |
 
 
 ## 引用

static/configs/gridmask/README.md

@@ -19,4 +19,4 @@
 
 | Backbone                | Type     | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           | Configs |
 | :---------------------- | :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
-| ResNet50-vd-FPN         | Faster     |     2     |   4x    |     21.847     |  39.1%  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_gridmask_4x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/gridmask/faster_rcnn_r50_vd_fpn_gridmask_4x.yml) |
+| ResNet50-vd-FPN         | Faster     |     2     |   4x    |     21.847     |  39.1%  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_gridmask_4x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/gridmask/faster_rcnn_r50_vd_fpn_gridmask_4x.yml) |

static/configs/hrnet/README.md

@@ -30,5 +30,5 @@
 
 | Backbone                | Type           | deformable Conv  | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           | Configs |
 | :---------------------- | :------------- | :---: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
-| HRNetV2p_W18            | Faster         | False |     2     |   1x    |     17.509     |  36.0  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.yml) |
-| HRNetV2p_W18            | Faster         | False |     2     |   2x    |     17.509     |  38.0  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x.yml) |
+| HRNetV2p_W18            | Faster         | False |     2     |   1x    |     17.509     |  36.0  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.yml) |
+| HRNetV2p_W18            | Faster         | False |     2     |   2x    |     17.509     |  38.0  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_hrnetv2p_w18_2x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x.yml) |

static/configs/iou_loss/README.md

@@ -43,6 +43,6 @@
 
 | Backbone                | Type        |   Loss Type | Loss Weight  | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           | Configs |
 | :---------------------- | :------------- | :---: | :---: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :---: |
-| ResNet50-vd-FPN            | Faster         | GIOU |   10   |    2     |   1x    |     22.94     |  39.4  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_giou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/iou_loss/faster_rcnn_r50_vd_fpn_giou_loss_1x.yml) |
-| ResNet50-vd-FPN            | Faster         | DIOU |   12   |    2     |   1x    |     22.94     |  39.2  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_diou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/iou_loss/faster_rcnn_r50_vd_fpn_diou_loss_1x.yml) |
-| ResNet50-vd-FPN            | Faster         | CIOU |   12   |    2     |   1x    |     22.95     |  39.6  |   -   | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_ciou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/iou_loss/faster_rcnn_r50_vd_fpn_ciou_loss_1x.yml) |
+| ResNet50-vd-FPN            | Faster         | GIOU |   10   |    2     |   1x    |     22.94     |  39.4  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_giou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/iou_loss/faster_rcnn_r50_vd_fpn_giou_loss_1x.yml) |
+| ResNet50-vd-FPN            | Faster         | DIOU |   12   |    2     |   1x    |     22.94     |  39.2  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_diou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/iou_loss/faster_rcnn_r50_vd_fpn_diou_loss_1x.yml) |
+| ResNet50-vd-FPN            | Faster         | CIOU |   12   |    2     |   1x    |     22.95     |  39.6  |   -   | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_ciou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/iou_loss/faster_rcnn_r50_vd_fpn_ciou_loss_1x.yml) |

static/configs/iou_loss/README_cn.md

@@ -83,9 +83,9 @@ CIOU loss使得在边框回归时，与目标框有重叠甚至包含时能够
 
 | 骨架网络             | 网络类型 | Loss类型 | Loss权重     | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | Mask AP |                           下载                          | 配置文件 |
 | :---------------------- | :------------- | :---: | :---: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :---: |
-| ResNet50-vd-FPN            | Faster         | GIOU |   10   |    2     |   1x    |     22.94     |  39.4  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_giou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/iou_loss/faster_rcnn_r50_vd_fpn_giou_loss_1x.yml) |
-| ResNet50-vd-FPN            | Faster         | DIOU |   12   |    2     |   1x    |     22.94     |  39.2  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_diou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/iou_loss/faster_rcnn_r50_vd_fpn_diou_loss_1x.yml) |
-| ResNet50-vd-FPN            | Faster         | CIOU |   12   |    2     |   1x    |     22.95     |  39.6  |   -   | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_ciou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/iou_loss/faster_rcnn_r50_vd_fpn_ciou_loss_1x.yml) |
+| ResNet50-vd-FPN            | Faster         | GIOU |   10   |    2     |   1x    |     22.94     |  39.4  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_giou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/iou_loss/faster_rcnn_r50_vd_fpn_giou_loss_1x.yml) |
+| ResNet50-vd-FPN            | Faster         | DIOU |   12   |    2     |   1x    |     22.94     |  39.2  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_diou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/iou_loss/faster_rcnn_r50_vd_fpn_diou_loss_1x.yml) |
+| ResNet50-vd-FPN            | Faster         | CIOU |   12   |    2     |   1x    |     22.95     |  39.6  |   -   | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_ciou_loss_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/iou_loss/faster_rcnn_r50_vd_fpn_ciou_loss_1x.yml) |
 
 
 

static/configs/libra_rcnn/README.md

@@ -19,5 +19,5 @@
 
 | Backbone                | Type     | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           | Configs |
 | :---------------------- | :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
-| ResNet50-vd-BFP         | Faster     |     2     |   1x    |     18.247     |  40.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r50_vd_fpn_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/libra_rcnn/libra_rcnn_r50_vd_fpn_1x.yml) |
-| ResNet101-vd-BFP         | Faster     |     2     |   1x    |     14.865     |  42.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r101_vd_fpn_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/libra_rcnn/libra_rcnn_r101_vd_fpn_1x.yml) |
+| ResNet50-vd-BFP         | Faster     |     2     |   1x    |     18.247     |  40.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r50_vd_fpn_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/libra_rcnn/libra_rcnn_r50_vd_fpn_1x.yml) |
+| ResNet101-vd-BFP         | Faster     |     2     |   1x    |     14.865     |  42.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r101_vd_fpn_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/libra_rcnn/libra_rcnn_r101_vd_fpn_1x.yml) |

static/configs/libra_rcnn/README_cn.md

@@ -60,8 +60,8 @@ Faster RCNN中生成许多候选框之后，使用随机的方法挑选正负样
 
 | 骨架网络             | 网络类型     | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | Mask AP |                           下载                          | 配置文件 |
 | :---------------------- | :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
-| ResNet50-vd-BFP         | Faster     |     2     |   1x    |     18.247     |  40.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r50_vd_fpn_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/libra_rcnn/libra_rcnn_r50_vd_fpn_1x.yml) |
-| ResNet101-vd-BFP         | Faster     |     2     |   1x    |     14.865     |  42.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r101_vd_fpn_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/libra_rcnn/libra_rcnn_r101_vd_fpn_1x.yml) |
+| ResNet50-vd-BFP         | Faster     |     2     |   1x    |     18.247     |  40.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r50_vd_fpn_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/libra_rcnn/libra_rcnn_r50_vd_fpn_1x.yml) |
+| ResNet101-vd-BFP         | Faster     |     2     |   1x    |     14.865     |  42.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/libra_rcnn_r101_vd_fpn_1x.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/libra_rcnn/libra_rcnn_r101_vd_fpn_1x.yml) |
 
 ## 引用
 

static/configs/random_erasing/README.md

@@ -18,4 +18,4 @@
 
 | Backbone                | Type     | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           | Configs |
 | :---------------------- | :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
-| ResNet50-vd-FPN         | Faster     |     2     |   4x    |     21.847     |  39.0%  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_random_erasing_4x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/random_erasing/faster_rcnn_r50_vd_fpn_random_erasing_4x.yml) |
+| ResNet50-vd-FPN         | Faster     |     2     |   4x    |     21.847     |  39.0%  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_r50_vd_fpn_random_erasing_4x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/random_erasing/faster_rcnn_r50_vd_fpn_random_erasing_4x.yml) |

static/configs/rcnn_enhance/README.md

@@ -32,9 +32,9 @@
 
 | 骨架网络             | 网络类型       | 每张GPU图片个数 | 学习率策略 |推理时间(fps) | Box AP | Mask AP |                           下载                          | 配置文件 |
 | :---------------------- | :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :-------------: | :-----: |
-| ResNet50-vd-FPN-Dcnv2         | Faster     |     2     |   3x    |     61.425     |  41.6  |    -    | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |
-| ResNet50-vd-FPN-Dcnv2         | Cascade Faster     |     2     |   3x    |     20.001     |  47.8  |    -    | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |
-| ResNet101-vd-FPN-Dcnv2         | Cascade Faster     |     2     |   3x    |     19.523     |  49.4  |    -    | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r101_vd_fpn_3x_server_side.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/cascade_rcnn_dcn_r101_vd_fpn_3x_server_side.yml) |
+| ResNet50-vd-FPN-Dcnv2         | Faster     |     2     |   3x    |     61.425     |  41.6  |    -    | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |
+| ResNet50-vd-FPN-Dcnv2         | Cascade Faster     |     2     |   3x    |     20.001     |  47.8  |    -    | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |
+| ResNet101-vd-FPN-Dcnv2         | Cascade Faster     |     2     |   3x    |     19.523     |  49.4  |    -    | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r101_vd_fpn_3x_server_side.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/cascade_rcnn_dcn_r101_vd_fpn_3x_server_side.yml) |
 
 
 **注**：generic文件夹下面的配置文件对应的预训练模型均只支持预测，不支持训练与评估。

static/configs/rcnn_enhance/README_en.md

@@ -36,9 +36,9 @@ And the following figure shows `mAP-Speed` curves for some common detectors.
 
 | Backbone                | Type     | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           | Configs |
 | :---------------------- | :-------------:  | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
-| ResNet50-vd-FPN-Dcnv2         | Faster     |     2     |   3x    |     61.425     |  41.6  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |
-| ResNet50-vd-FPN-Dcnv2         | Cascade Faster     |     2     |   3x    |     20.001     |  47.8  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |
-| ResNet101-vd-FPN-Dcnv2         | Cascade Faster     |     2     |   3x    |     19.523     |  49.4  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r101_vd_fpn_3x_server_side.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/cascade_rcnn_dcn_r101_vd_fpn_3x_server_side.yml) |
+| ResNet50-vd-FPN-Dcnv2         | Faster     |     2     |   3x    |     61.425     |  41.6  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/faster_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |
+| ResNet50-vd-FPN-Dcnv2         | Cascade Faster     |     2     |   3x    |     20.001     |  47.8  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/cascade_rcnn_dcn_r50_vd_fpn_3x_server_side.yml) |
+| ResNet101-vd-FPN-Dcnv2         | Cascade Faster     |     2     |   3x    |     19.523     |  49.4  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r101_vd_fpn_3x_server_side.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/cascade_rcnn_dcn_r101_vd_fpn_3x_server_side.yml) |
 
 
 **Attention**: Pretrained models whose congigurations are in the directory `generic` just support inference but do not support training and evaluation as now.

static/configs/res2net/README.md

@@ -30,7 +30,7 @@
 
 | Backbone                | Type           | deformable Conv  | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP |                           Download                           | Configs |
 | :---------------------- | :------------- | :---: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
-| Res2Net50-FPN            | Faster         | False |     2     |   1x    |     20.320     |  39.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_res2net50_vb_26w_4s_fpn_1x.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_1x.yml) |
-| Res2Net50-FPN            | Mask         | False |     2     |   2x    |     16.069     |  40.7  |    36.2    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vb_26w_4s_fpn_2x.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_2x.yml) |
-| Res2Net50-vd-FPN            | Mask         | False |     2     |   2x    |     15.816     |  40.9  |    36.2    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vd_26w_4s_fpn_2x.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_2x.yml) |
-| Res2Net50-vd-FPN            | Mask         | True |     2     |   2x    |     14.478     |  43.5  |    38.4    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vd_26w_4s_fpn_dcnv2_1x.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_dcnv2_1x.yml) |
+| Res2Net50-FPN            | Faster         | False |     2     |   1x    |     20.320     |  39.5  |    -    | [model](https://paddlemodels.bj.bcebos.com/object_detection/faster_rcnn_res2net50_vb_26w_4s_fpn_1x.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_1x.yml) |
+| Res2Net50-FPN            | Mask         | False |     2     |   2x    |     16.069     |  40.7  |    36.2    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vb_26w_4s_fpn_2x.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_2x.yml) |
+| Res2Net50-vd-FPN            | Mask         | False |     2     |   2x    |     15.816     |  40.9  |    36.2    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vd_26w_4s_fpn_2x.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_2x.yml) |
+| Res2Net50-vd-FPN            | Mask         | True |     2     |   2x    |     14.478     |  43.5  |    38.4    | [model](https://paddlemodels.bj.bcebos.com/object_detection/mask_rcnn_res2net50_vd_26w_4s_fpn_dcnv2_1x.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_dcnv2_1x.yml) |

static/configs/solov2/README.md

@@ -25,15 +25,15 @@ SOLOv2 (Segmenting Objects by Locations) is a fast instance segmentation framewo
 | BlendMask | R50-FPN | True        |   3x    |     37.8        |  13.5  | V100 |   -  |  -  |
 | SOLOv2 (Paper) | R50-FPN | False        |   1x    |     34.8        |  18.5  | V100 |   -  |  -  |
 | SOLOv2 (Paper) | X101-DCN-FPN | True        |   3x    |     42.4        |  5.9  | V100 |   -  |  -  |
-| SOLOv2 | Mobilenetv3-FPN                 |  True                |   3x    |    30.0          |  50    | V100 | [model](https://paddlemodels.bj.bcebos.com/object_detection/solov2_mobilenetv3_fpn_448_3x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/solov2/solov2_mobilenetv3_fpn_448_3x.yml) |
-| SOLOv2 | R50-FPN                 |  False                |   1x    |    35.6         |  21.9     | V100 |  [model](https://paddlemodels.bj.bcebos.com/object_detection/solov2_r50_fpn_1x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/solov2/solov2_r50_fpn_1x.yml) |
-| SOLOv2 | R50-FPN                 |  True                |   3x    |     37.9         |   21.9    | V100 |  [model](https://paddlemodels.bj.bcebos.com/object_detection/solov2_r50_fpn_3x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/solov2/solov2_r50_fpn_3x.yml) |
-| SOLOv2 | R101-VD-FPN                 |  True               |   3x    |   42.6        |  12.1     | V100 |  [model](https://paddlemodels.bj.bcebos.com/object_detection/solov2_r101_vd_fpn_3x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/solov2/solov2_r101_vd_fpn_3x.yml) |
+| SOLOv2 | Mobilenetv3-FPN                 |  True                |   3x    |    30.0          |  50    | V100 | [model](https://paddlemodels.bj.bcebos.com/object_detection/solov2_mobilenetv3_fpn_448_3x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/solov2/solov2_mobilenetv3_fpn_448_3x.yml) |
+| SOLOv2 | R50-FPN                 |  False                |   1x    |    35.6         |  21.9     | V100 |  [model](https://paddlemodels.bj.bcebos.com/object_detection/solov2_r50_fpn_1x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/solov2/solov2_r50_fpn_1x.yml) |
+| SOLOv2 | R50-FPN                 |  True                |   3x    |     37.9         |   21.9    | V100 |  [model](https://paddlemodels.bj.bcebos.com/object_detection/solov2_r50_fpn_3x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/solov2/solov2_r50_fpn_3x.yml) |
+| SOLOv2 | R101-VD-FPN                 |  True               |   3x    |   42.6        |  12.1     | V100 |  [model](https://paddlemodels.bj.bcebos.com/object_detection/solov2_r101_vd_fpn_3x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/solov2/solov2_r101_vd_fpn_3x.yml) |
 
 ## Enhanced model
 | Backbone                | Input size  | Lr schd | V100 FP32(FPS) | Mask AP<sup>val</sup> |         Download                  | Configs |
 | :---------------------: | :-------------------: | :-----: | :------------: | :-----: | :---------: | :------------------------: |
-| Light-R50-VD-DCN-FPN          |  512     |   3x    |     38.6          |  38.8   | [model](https://paddlemodels.bj.bcebos.com/object_detection/solov2_light_r50_vd_fpn_dcn_512_3x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/solov2/solov2_light_r50_vd_fpn_dcn_512_3x.yml) |
+| Light-R50-VD-DCN-FPN          |  512     |   3x    |     38.6          |  38.8   | [model](https://paddlemodels.bj.bcebos.com/object_detection/solov2_light_r50_vd_fpn_dcn_512_3x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/solov2/solov2_light_r50_vd_fpn_dcn_512_3x.yml) |
 
 **Notes:**
 

static/configs/yolov4/README.md

@@ -41,8 +41,8 @@ python tools/anchor_cluster.py -c ${config} -m ${method} -s ${size}
 
 |                          | GPU个数 | 测试集  | 骨干网络 |  精度  | 模型下载 |  配置文件  |
 |:------------------------:|:-------:|:------:|:--------------------------:|:------------------------:| :---------:| :-----: |
-| YOLO v4  | - |test-dev2019        |     CSPDarkNet53 |  43.5 |[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_cspdarknet.yml)                   |
-| YOLO v4 VOC  | 2 | VOC2007        |     CSPDarkNet53 |  85.5  |   [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet_voc.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov4/yolov4_cspdarknet_voc.yml)              |
+| YOLO v4  | - |test-dev2019        |     CSPDarkNet53 |  43.5 |[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/yolov4/yolov4_cspdarknet.yml)                   |
+| YOLO v4 VOC  | 2 | VOC2007        |     CSPDarkNet53 |  85.5  |   [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov4_cspdarknet_voc.pdparams) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/yolov4/yolov4_cspdarknet_voc.yml)              |
 
 **注意:**
 

static/deploy/README.md

@@ -8,7 +8,7 @@
 ## 模型导出
 训练得到一个满足要求的模型后，如果想要将该模型接入到C++服务器端预测库或移动端预测库，需要通过`tools/export_model.py`导出该模型。
 
-- [导出教程](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
+- [导出教程](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
 
 模型导出后, 目录结构如下(以`yolov3_darknet`为例):
 ```
@@ -21,8 +21,8 @@ yolov3_darknet # 模型目录
 预测时，该目录所在的路径会作为程序的输入参数。
 
 ## 预测部署
-- [1. Python预测(支持 Linux 和 Windows)](https://github.com/PaddlePaddle/PaddleDetection/blob/master/deploy/python)
-- [2. C++预测(支持 Linux 和 Windows)](https://github.com/PaddlePaddle/PaddleDetection/blob/master/deploy/cpp)
+- [1. Python预测(支持 Linux 和 Windows)](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/deploy/python)
+- [2. C++预测(支持 Linux 和 Windows)](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/deploy/cpp)
 - [3. 在线服务化部署](./serving/README.md)
 - [4. 移动端部署](https://github.com/PaddlePaddle/Paddle-Lite-Demo)
 - [5. Jetson设备部署](./cpp/docs/Jetson_build.md)

static/deploy/cpp/README.md

@@ -52,7 +52,7 @@ deploy/cpp
 ## 3.编译部署
 
 ### 3.1 导出模型
-请确认您已经基于`PaddleDetection`的[export_model.py](https://github.com/PaddlePaddle/PaddleDetection/blob/master/tools/export_model.py)导出您的模型，并妥善保存到合适的位置。导出模型细节请参考 [导出模型教程](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/advanced_tutorials/deploy/EXPORT_MODEL.md)。
+请确认您已经基于`PaddleDetection`的[export_model.py](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/tools/export_model.py)导出您的模型，并妥善保存到合适的位置。导出模型细节请参考 [导出模型教程](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/advanced_tutorials/deploy/EXPORT_MODEL.md)。
 
 模型导出后, 目录结构如下(以`yolov3_darknet`为例):
 ```

static/deploy/python/README.md

@@ -3,7 +3,7 @@
 Python预测可以使用`tools/infer.py`，此种方式依赖PaddleDetection源码；也可以使用本篇教程预测方式，先将模型导出，使用一个独立的文件进行预测。
 
 
-本篇教程使用AnalysisPredictor对[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/advanced_tutorials/deploy/EXPORT_MODEL.md)进行高性能预测。
+本篇教程使用AnalysisPredictor对[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/advanced_tutorials/deploy/EXPORT_MODEL.md)进行高性能预测。
 
 在PaddlePaddle中预测引擎和训练引擎底层有着不同的优化方法, 下面列出了两种不同的预测方式。Executor同时支持训练和预测，AnalysisPredictor则专门针对推理进行了优化，是基于[C++预测库](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/inference_deployment/inference/native_infer.html)的Python接口，该引擎可以对模型进行多项图优化，减少不必要的内存拷贝。如果用户在部署已训练模型的过程中对性能有较高的要求，我们提供了独立于PaddleDetection的预测脚本，方便用户直接集成部署。
 
@@ -18,7 +18,7 @@ Python预测可以使用`tools/infer.py`，此种方式依赖PaddleDetection源
 
 ## 1. 导出预测模型
 
-PaddleDetection在训练过程包括网络的前向和优化器相关参数，而在部署过程中，我们只需要前向参数，具体参考:[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
+PaddleDetection在训练过程包括网络的前向和优化器相关参数，而在部署过程中，我们只需要前向参数，具体参考:[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
 
 导出后目录下，包括`__model__`，`__params__`和`infer_cfg.yml`三个文件。
 

static/deploy/serving/README.md

@@ -22,7 +22,7 @@ pip install paddle-serving-server-gpu -i https://mirror.baidu.com/pypi/simple
 ```
 
 ## 3. 导出模型
-PaddleDetection在训练过程包括网络的前向和优化器相关参数，而在部署过程中，我们只需要前向参数，具体参考:[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
+PaddleDetection在训练过程包括网络的前向和优化器相关参数，而在部署过程中，我们只需要前向参数，具体参考:[导出模型](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/advanced_tutorials/deploy/EXPORT_MODEL.md)
 
 ```
 python tools/export_serving_model.py -c configs/yolov3_mobilenet_v1_roadsign.yml -o use_gpu=true weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_roadsign.pdparams --output_dir=./inference_model

static/docs/advanced_tutorials/MODEL_TECHNICAL.md

@@ -34,7 +34,7 @@ PaddleDetection的网络模型模块所有代码逻辑在`ppdet/modeling/`中，
 ![](../images/models_figure.png)
 
 ## 新增模型
-我们以单阶段检测器YOLOv3为例，结合[yolov3_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml)配置文件，对建立模型过程进行详细描述，
+我们以单阶段检测器YOLOv3为例，结合[yolov3_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/yolov3_darknet.yml)配置文件，对建立模型过程进行详细描述，
 按照此思路您可以快速搭建新的模型。
 
 搭建新模型的一般步骤是：Backbone编写、检测组件编写与模型组网这三个步骤，下面为您详细介绍：

static/docs/advanced_tutorials/READER.md

@@ -392,7 +392,7 @@ EvalReader：
 `DataLoader`的API详见[fluid.io.DataLoader](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/api_cn/io_cn/DataLoader_cn.html#dataloader)。
 具体步骤如下：
 
-- 在[train.py](https://github.com/PaddlePaddle/PaddleDetection/blob/master/tools/train.py)、[eval.py](https://github.com/PaddlePaddle/PaddleDetection/blob/master/tools/eval.py)和[infer.py](https://github.com/PaddlePaddle/PaddleDetection/blob/master/tools/infer.py)里创建训练时的Reader：
+- 在[train.py](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/tools/train.py)、[eval.py](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/tools/eval.py)和[infer.py](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/tools/infer.py)里创建训练时的Reader：
 ```python
 # 创建DataLoader对象
 inputs_def = cfg['TestReader']['inputs_def']

static/docs/advanced_tutorials/TRANSFER_LEARNING.md

@@ -11,7 +11,7 @@ In transfer learning, if different dataset and the number of classes is used, th
 Transfer learning needs custom dataset and annotation in COCO-format and VOC-format is supported now. The script converts the annotation from voc, labelme or cityscape to COCO is provided in ```tools/x2coco.py```. More details please refer to [READER](READER.md). After data preparation, update the data parameters in configuration file.
 
 
-1. COCO-format dataset, take [yolov3\_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml#L66) for example, modify the COCODataSet in yolov3\_reader:
+1. COCO-format dataset, take [yolov3\_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/yolov3_darknet.yml#L66) for example, modify the COCODataSet in yolov3\_reader:
 
 ```yml
   dataset:
@@ -22,7 +22,7 @@ Transfer learning needs custom dataset and annotation in COCO-format and VOC-for
       with_background: false
 ```
 
-2. VOC-format dataset, take [yolov3\_darknet\_voc.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet_voc.yml#L67) for example, modify the VOCDataSet in the configuration:
+2. VOC-format dataset, take [yolov3\_darknet\_voc.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/yolov3_darknet_voc.yml#L67) for example, modify the VOCDataSet in the configuration:
 
 ```yml
   dataset:
@@ -52,7 +52,7 @@ python -u tools/train.py -c configs/faster_rcnn_r50_1x.yml \
 
 The parameters which need to ignore can be specified explicitly as well and arbitrary parameter names can be added to `finetune_exclude_pretrained_params`. For this purpose, several methods can be used as follwed:
 
-- Set `finetune_exclude_pretrained_params` in YAML configuration files. Please refer to [configure file](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_mobilenet_v1_fruit.yml#L15)
+- Set `finetune_exclude_pretrained_params` in YAML configuration files. Please refer to [configure file](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/yolov3_mobilenet_v1_fruit.yml#L15)
 - Set `finetune_exclude_pretrained_params` in command line. For example:
 
 ```python

static/docs/advanced_tutorials/TRANSFER_LEARNING_cn.md

@@ -9,7 +9,7 @@
 
 迁移学习需要使用自己的数据集，目前已支持COCO和VOC的数据标注格式，在```tools/x2coco.py```中给出了voc、labelme和cityscape标注格式转换为COCO格式的脚本，具体使用方式可以参考[自定义数据源](READER.md)。数据准备完成后，在配置文件中配置数据路径，对应修改reader中的路径参数即可。
 
-1. COCO数据集需要修改COCODataSet中的参数，以[yolov3\_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml#L66)为例，修改yolov3\_reader中的配置：
+1. COCO数据集需要修改COCODataSet中的参数，以[yolov3\_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/yolov3_darknet.yml#L66)为例，修改yolov3\_reader中的配置：
 
 ```yml
   dataset:
@@ -20,7 +20,7 @@
       with_background: false
 ```
 
-2. VOC数据集需要修改VOCDataSet中的参数，以[yolov3\_darknet\_voc.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet_voc.yml#L67)为例：
+2. VOC数据集需要修改VOCDataSet中的参数，以[yolov3\_darknet\_voc.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/yolov3_darknet_voc.yml#L67)为例：
 
 ```yml
   dataset:
@@ -56,7 +56,7 @@ python -u tools/train.py -c configs/faster_rcnn_r50_1x.yml \
 
 可以显示的指定训练过程中忽略参数的名字，任何参数名均可加入`finetune_exclude_pretrained_params`中，为实现这一目的，可通过如下方式实现：
 
-1. 在 YMAL 配置文件中通过设置`finetune_exclude_pretrained_params`字段。可参考[配置文件](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_mobilenet_v1_fruit.yml#L15)
+1. 在 YMAL 配置文件中通过设置`finetune_exclude_pretrained_params`字段。可参考[配置文件](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/yolov3_mobilenet_v1_fruit.yml#L15)
 2. 在 train.py的启动参数中设置`finetune_exclude_pretrained_params`。例如：
 
 ```python

static/docs/advanced_tutorials/deploy/docs/windows_vs2015_build.md

 # Windows平台使用 Visual Studio 2015 编译指南
 
-本文档步骤，我们同时在`Visual Studio 2015` 和 `Visual Studio 2019 Community` 两个版本进行了测试，我们推荐使用[`Visual Studio 2019`直接编译`CMake`项目](https://github.com/PaddlePaddle/PaddleDetection/blob/master/deploy/cpp/docs/windows_vs2019_build.md)。
+本文档步骤，我们同时在`Visual Studio 2015` 和 `Visual Studio 2019 Community` 两个版本进行了测试，我们推荐使用[`Visual Studio 2019`直接编译`CMake`项目](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/deploy/cpp/docs/windows_vs2019_build.md)。
 
 
 ## 前置条件

static/docs/conf.py

@@ -162,7 +162,7 @@ texinfo_documents = [
 def url_resolver(url):
     if ".html" not in url:
         url = url.replace("../", "")
-        return "https://github.com/PaddlePaddle/PaddleDetection/tree/master" + url
+        return "https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static" + url
     else:
         if DEPLOY:
             return "http://paddledetection.readthedocs.io/" + url

static/docs/featured_model/CONTRIB.md

@@ -5,8 +5,8 @@ We provide some models implemented by PaddlePaddle to detect objects in specific
 
 | Task                 | Algorithm | Box AP | Download                                                                                | Configs |
 |:---------------------|:---------:|:------:| :-------------------------------------------------------------------------------------: |:------:|
-| Vehicle Detection    |  YOLOv3  |  54.5  | [model](https://paddlemodels.bj.bcebos.com/object_detection/vehicle_yolov3_darknet.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/contrib/VehicleDetection/vehicle_yolov3_darknet.yml) |
-| Pedestrian Detection |  YOLOv3  |  51.8  | [model](https://paddlemodels.bj.bcebos.com/object_detection/pedestrian_yolov3_darknet.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/contrib/PedestrianDetection/pedestrian_yolov3_darknet.yml) |
+| Vehicle Detection    |  YOLOv3  |  54.5  | [model](https://paddlemodels.bj.bcebos.com/object_detection/vehicle_yolov3_darknet.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/contrib/VehicleDetection/vehicle_yolov3_darknet.yml) |
+| Pedestrian Detection |  YOLOv3  |  51.8  | [model](https://paddlemodels.bj.bcebos.com/object_detection/pedestrian_yolov3_darknet.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/contrib/PedestrianDetection/pedestrian_yolov3_darknet.yml) |
 
 ## Vehicle Detection
 
@@ -18,7 +18,7 @@ The network for detecting vehicles is YOLOv3, the backbone of which is Dacknet53
 
 ### 2. Configuration for training
 
-PaddleDetection provides users with a configuration file [yolov3_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for vehicle detection:
+PaddleDetection provides users with a configuration file [yolov3_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/yolov3_darknet.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for vehicle detection:
 
 * max_iters: 120000
 * num_classes: 6
@@ -67,7 +67,7 @@ The network for detecting vehicles is YOLOv3, the backbone of which is Dacknet53
 
 ### 2. Configuration for training
 
-PaddleDetection provides users with a configuration file [yolov3_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for pedestrian detection:
+PaddleDetection provides users with a configuration file [yolov3_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/yolov3_darknet.yml) to train YOLOv3 on the COCO dataset, compared with this file, we modify some parameters as followed to conduct the training for pedestrian detection:
 
 * max_iters: 200000
 * num_classes: 1

static/docs/featured_model/CONTRIB_cn.md

@@ -5,8 +5,8 @@
 
 | 任务                 | 算法 | 精度(Box AP) | 下载                                                                                | 配置文件 |
 |:---------------------|:---------:|:------:| :---------------------------------------------------------------------------------: | :------:|
-| 车辆检测    |  YOLOv3  |  54.5  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/vehicle_yolov3_darknet.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/contrib/VehicleDetection/vehicle_yolov3_darknet.yml) |
-| 行人检测 |  YOLOv3  |  51.8  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/pedestrian_yolov3_darknet.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/contrib/PedestrianDetection/pedestrian_yolov3_darknet.yml) |
+| 车辆检测    |  YOLOv3  |  54.5  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/vehicle_yolov3_darknet.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/contrib/VehicleDetection/vehicle_yolov3_darknet.yml) |
+| 行人检测 |  YOLOv3  |  51.8  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/pedestrian_yolov3_darknet.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/contrib/PedestrianDetection/pedestrian_yolov3_darknet.yml) |
 
 
 ## 车辆检测（Vehicle Detection）
@@ -19,7 +19,7 @@ Backbone为Dacknet53的YOLOv3。
 
 ### 2. 训练参数配置
 
-PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darnet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml)，与之相比，在进行车辆检测的模型训练时，我们对以下参数进行了修改：
+PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darnet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/yolov3_darknet.yml)，与之相比，在进行车辆检测的模型训练时，我们对以下参数进行了修改：
 
 * max_iters: 120000
 * num_classes: 6
@@ -69,7 +69,7 @@ Backbone为Dacknet53的YOLOv3。
 
 ### 2. 训练参数配置
 
-PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml)，与之相比，在进行行人检测的模型训练时，我们对以下参数进行了修改：
+PaddleDetection提供了使用COCO数据集对YOLOv3进行训练的参数配置文件[yolov3_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/yolov3_darknet.yml)，与之相比，在进行行人检测的模型训练时，我们对以下参数进行了修改：
 
 * max_iters: 200000
 * num_classes: 1

static/docs/featured_model/FACE_DETECTION.md

@@ -34,16 +34,16 @@ FaceDetection的目标是提供高效、高速的人脸检测解决方案，包
 
 | 网络结构 | 类型     | 输入尺寸 | 图片个数/GPU | 学习率策略 | Easy Set  | Medium Set | Hard Set  | 下载 | 配置文件 |
 |:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:|:--------:|
-| BlazeFace    | 原始版本 | 640  |    8    | 32w     | **0.915** | **0.892**  | **0.797** | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface.yml) |
-| BlazeFace    | Lite版本    | 640  |    8    | 32w     | 0.909     | 0.885      | 0.781     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface.yml) |
-| BlazeFace    | NAS版本    | 640  |    8    | 32w     | 0.837     | 0.807      | 0.658     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface_nas.yml) |
-| BlazeFace    | NAS_V2版本 | 640  |    8    | 32W     | 0.870     | 0.837      | 0.685     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface_nas_v2.yml) |
-| FaceBoxes    | 原始版本 | 640  |    8    | 32w     | 0.878     | 0.851      | 0.576     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_original.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/faceboxes.yml) |
-| FaceBoxes    | Lite版本   | 640  |    8    | 32w     | 0.901     | 0.875      | 0.760     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_lite.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/faceboxes_lite.yml) |
+| BlazeFace    | 原始版本 | 640  |    8    | 32w     | **0.915** | **0.892**  | **0.797** | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/blazeface.yml) |
+| BlazeFace    | Lite版本    | 640  |    8    | 32w     | 0.909     | 0.885      | 0.781     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/blazeface.yml) |
+| BlazeFace    | NAS版本    | 640  |    8    | 32w     | 0.837     | 0.807      | 0.658     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/blazeface_nas.yml) |
+| BlazeFace    | NAS_V2版本 | 640  |    8    | 32W     | 0.870     | 0.837      | 0.685     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/blazeface_nas_v2.yml) |
+| FaceBoxes    | 原始版本 | 640  |    8    | 32w     | 0.878     | 0.851      | 0.576     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_original.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/faceboxes.yml) |
+| FaceBoxes    | Lite版本   | 640  |    8    | 32w     | 0.901     | 0.875      | 0.760     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_lite.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/faceboxes_lite.yml) |
 
 **注意:**  
 - 我们使用`tools/face_eval.py`中多尺度评估策略得到`Easy/Medium/Hard Set`里的mAP。具体细节请参考[在WIDER-FACE数据集上评估](#在WIDER-FACE数据集上评估)。
-- BlazeFace-Lite的训练与测试使用 [blazeface.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/face_detection/blazeface.yml)配置文件并且设置：`lite_edition: true`。
+- BlazeFace-Lite的训练与测试使用 [blazeface.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/face_detection/blazeface.yml)配置文件并且设置：`lite_edition: true`。
 
 #### FDDB数据集上的mAP
 
@@ -258,7 +258,7 @@ wget https://dataset.bj.bcebos.com/wider_face/wider_face_train_bbx_lmk_gt.txt
 
 | 网络结构     | 输入尺寸 | 图片个数/GPU | 学习率策略 | Easy Set  | Medium Set | Hard Set  | 下载 | 配置文件 |
 |:------------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:|:--------:|
-| BlazeFace Keypoint     | 640  |    16    | 16w     | 0.852     | 0.816      | 0.662     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_keypoint.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface_keypoint.yml) |
+| BlazeFace Keypoint     | 640  |    16    | 16w     | 0.852     | 0.816      | 0.662     | [模型](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_keypoint.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/blazeface_keypoint.yml) |
 
 ![](../images/12_Group_Group_12_Group_Group_12_84.jpg)
 
@@ -292,7 +292,7 @@ wget https://dataset.bj.bcebos.com/wider_face/wider_face_train_bbx_lmk_gt.txt
 
 **版本信息:**
 - 原始版本: 参考原始论文进行修改；
-- Lite版本: 使用更少的网络层数和通道数，具体可参考[代码](https://github.com/PaddlePaddle/PaddleDetection/blob/master/ppdet/modeling/architectures/faceboxes.py)。
+- Lite版本: 使用更少的网络层数和通道数，具体可参考[代码](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/ppdet/modeling/architectures/faceboxes.py)。
 
 
 ## 如何贡献代码

static/docs/featured_model/FACE_DETECTION_en.md

@@ -38,17 +38,17 @@ optimized network structure.
 
 | Architecture | Type     | Size | Img/gpu | Lr schd | Easy Set  | Medium Set | Hard Set  | Download | Configs |
 |:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:|:--------:|
-| BlazeFace    | Original | 640  |    8    | 32w     | **0.915** | **0.892**  | **0.797** | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface.yml) |
-| BlazeFace    | Lite     | 640  |    8    | 32w     | 0.909     | 0.885      | 0.781     | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface.yml) |
-| BlazeFace    | NAS      | 640  |    8    | 32w     | 0.837     | 0.807      | 0.658     | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface_nas.yml) |
-| BlazeFace    | NAS_V2   | 640  |    8    | 32W     | 0.870     | 0.837      | 0.685     | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface_nas_v2.yml) |
-| FaceBoxes    | Original | 640  |    8    | 32w     | 0.878     | 0.851      | 0.576     | [model](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_original.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/faceboxes.yml) |
-| FaceBoxes    | Lite     | 640  |    8    | 32w     | 0.901     | 0.875      | 0.760     | [model](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_lite.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/faceboxes_lite.yml) |
+| BlazeFace    | Original | 640  |    8    | 32w     | **0.915** | **0.892**  | **0.797** | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_original.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/blazeface.yml) |
+| BlazeFace    | Lite     | 640  |    8    | 32w     | 0.909     | 0.885      | 0.781     | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_lite.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/blazeface.yml) |
+| BlazeFace    | NAS      | 640  |    8    | 32w     | 0.837     | 0.807      | 0.658     | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/blazeface_nas.yml) |
+| BlazeFace    | NAS_V2   | 640  |    8    | 32W     | 0.870     | 0.837      | 0.685     | [model](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_nas2.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/blazeface_nas_v2.yml) |
+| FaceBoxes    | Original | 640  |    8    | 32w     | 0.878     | 0.851      | 0.576     | [model](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_original.tar) |  [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/faceboxes.yml) |
+| FaceBoxes    | Lite     | 640  |    8    | 32w     | 0.901     | 0.875      | 0.760     | [model](https://paddlemodels.bj.bcebos.com/object_detection/faceboxes_lite.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/faceboxes_lite.yml) |
 
 **NOTES:**  
 - Get mAP in `Easy/Medium/Hard Set` by multi-scale evaluation in `tools/face_eval.py`.
 For details can refer to [Evaluation](#Evaluate-on-the-WIDER-FACE).
-- BlazeFace-Lite Training and Testing ues [blazeface.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/face_detection/blazeface.yml)
+- BlazeFace-Lite Training and Testing ues [blazeface.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/face_detection/blazeface.yml)
 configs file and set `lite_edition: true`.
 
 #### mAP in FDDB
@@ -274,7 +274,7 @@ wget https://dataset.bj.bcebos.com/wider_face/wider_face_train_bbx_lmk_gt.txt
 
 | Architecture  | Size | Img/gpu | Lr schd | Easy Set  | Medium Set | Hard Set  | Download | Configs |
 |:------------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:|:--------:|
-| BlazeFace Keypoint     | 640  |    16    | 16w     | 0.852     | 0.816      | 0.662     | [download](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_keypoint.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/face_detection/blazeface_keypoint.yml) |
+| BlazeFace Keypoint     | 640  |    16    | 16w     | 0.852     | 0.816      | 0.662     | [download](https://paddlemodels.bj.bcebos.com/object_detection/blazeface_keypoint.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/face_detection/blazeface_keypoint.yml) |
 
 ![](../images/12_Group_Group_12_Group_Group_12_84.jpg)
 

static/docs/featured_model/LARGE_SCALE_DET_MODEL.md

@@ -20,6 +20,6 @@
 
 | 骨架网络       | 网络类型     |      下载       | 配置文件 |
 | :---------------| :---------------| :---------------| :---------------
-| ResNet50-vd-FPN-Dcnv2         | Cascade Faster     |  [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_gen_server_side.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/generic/cascade_rcnn_dcn_r50_vd_fpn_gen_server_side.yml) |
-| ResNet101-vd-FPN-Dcnv2         | Cascade Faster     |  [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r101_vd_fpn_gen_server_side.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/generic/cascade_rcnn_dcn_r101_vd_fpn_gen_server_side.yml) |
-| CBResNet101-vd-FPN         | Cascade Faster     |  [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_cbr101_vd_fpn_server_side.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/generic/cascade_rcnn_cbr101_vd_fpn_server_side.yml) |
+| ResNet50-vd-FPN-Dcnv2         | Cascade Faster     |  [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_gen_server_side.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/generic/cascade_rcnn_dcn_r50_vd_fpn_gen_server_side.yml) |
+| ResNet101-vd-FPN-Dcnv2         | Cascade Faster     |  [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r101_vd_fpn_gen_server_side.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/generic/cascade_rcnn_dcn_r101_vd_fpn_gen_server_side.yml) |
+| CBResNet101-vd-FPN         | Cascade Faster     |  [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_cbr101_vd_fpn_server_side.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/generic/cascade_rcnn_cbr101_vd_fpn_server_side.yml) |

static/docs/featured_model/LARGE_SCALE_DET_MODEL_en.md

@@ -18,6 +18,6 @@ There are relatively not enough categories in the above dataset (compared to 100
 
 | Backbone       | Type     |      Download       | Configs |
 | :---------------| :---------------| :---------------| :---------------
-| ResNet50-vd-FPN-Dcnv2         | Cascade Faster     |  [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_gen_server_side.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/generic/cascade_rcnn_dcn_r50_vd_fpn_gen_server_side.yml) |
-| ResNet101-vd-FPN-Dcnv2         | Cascade Faster     |  [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r101_vd_fpn_gen_server_side.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/generic/cascade_rcnn_dcn_r101_vd_fpn_gen_server_side.yml) |
-| CBResNet101-vd-FPN         | Cascade Faster     |  [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_cbr101_vd_fpn_server_side.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/rcnn_enhance/generic/cascade_rcnn_cbr101_vd_fpn_server_side.yml) |
+| ResNet50-vd-FPN-Dcnv2         | Cascade Faster     |  [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r50_vd_fpn_gen_server_side.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/generic/cascade_rcnn_dcn_r50_vd_fpn_gen_server_side.yml) |
+| ResNet101-vd-FPN-Dcnv2         | Cascade Faster     |  [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcn_r101_vd_fpn_gen_server_side.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/generic/cascade_rcnn_dcn_r101_vd_fpn_gen_server_side.yml) |
+| CBResNet101-vd-FPN         | Cascade Faster     |  [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_cbr101_vd_fpn_server_side.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/rcnn_enhance/generic/cascade_rcnn_cbr101_vd_fpn_server_side.yml) |

static/docs/featured_model/YOLOv3_ENHANCEMENT.md

@@ -43,12 +43,12 @@ python tools/train.py -c configs/dcn/yolov3_r50vd_dcn_db_iouloss_obj365_pretrain
 
 |                   模型                   |                          预训练模型                          | 验证集 mAP |     V100 python 预测速度（FPS）<sup>[1](#1)</sup>    |   V100 paddle预测库速度（ms/image）<sup>[2](#2)</sup> |  P4 paddle预测库速度(ms/image) <sup>[2](#2)</sup>  |          下载                | 配置文件 |
 | :--------------------------------------: | :----------------------------------------------------------: | :--------: | :--------: | :------------------------------------: | :----------------------------------------------------------: | :--------: | :--------: |
-|              YOLOv3 DarkNet              | [DarkNet pretrain](https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar) |    38.9   | 48.55 |  原生：19.63<br>tensorRT-FP32: 18.01<br>tensorRT-FP16: 11.47 | 原生：54.10<br>tensorRT-FP32: 47.44  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/yolov3_darknet.yml) |
-|          YOLOv3 ResNet50_vd DCN          | [ImageNet pretrain](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) |    39.1    | 50.80 | 原生：17.04<br>tensorRT-FP32: 16.28<br>tensorRT-FP16: 11.16 | 原生：40.01<br>tensorRT-FP32: 36.66  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_imagenet.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/yolov3_r50vd_dcn.yml) |
-|          YOLOv3 ResNet50_vd DCN          | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) |    42.5    | 50.41 |  原生：16.76<br>tensorRT-FP32: 16.04<br>tensorRT-FP16: 10.70 | 原生：39.64<br>tensorRT-FP32: 35.93  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_v2.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/yolov3_r50vd_dcn_obj365_pretrained_coco.yml) |
-|     YOLOv3 ResNet50_vd DCN DropBlock     | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) |     42.8  | 49.97  |  原生：16.55<br>tensorRT-FP32: 16.07<br>tensorRT-FP16: 10.69 | 原生：39.72<br/>tensorRT-FP32: 35.98 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_dropblock.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/yolov3_r50vd_dcn_db_obj365_pretrained_coco.yml) |
-| YOLOv3 ResNet50_vd DCN DropBlock IoULoss | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) |    43.2    | 49.91 |  原生：16.46<br>tensorRT-FP32: 15.83<br>tensorRT-FP16: 10.80 | 原生：39.58<br/>tensorRT-FP32: 35.61 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_dropblock_iouloss.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/yolov3_r50vd_dcn_db_iouloss_obj365_pretrained_coco.yml) |
-| YOLOv3 ResNet50_vd DCN DropBlock IoU-Aware | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) |    43.6    | 48.19 |  原生：17.74<br>tensorRT-FP32: 16.73<br>tensorRT-FP16: 11.74 | 原生：41.39<br/>tensorRT-FP32: 37.75 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_db_iouaware_obj365_pretrained_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/yolov3_r50vd_dcn_db_iouaware_obj365_pretrained_coco.yml) |
+|              YOLOv3 DarkNet              | [DarkNet pretrain](https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_pretrained.tar) |    38.9   | 48.55 |  原生：19.63<br>tensorRT-FP32: 18.01<br>tensorRT-FP16: 11.47 | 原生：54.10<br>tensorRT-FP32: 47.44  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_darknet.tar) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/yolov3_darknet.yml) |
+|          YOLOv3 ResNet50_vd DCN          | [ImageNet pretrain](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) |    39.1    | 50.80 | 原生：17.04<br>tensorRT-FP32: 16.28<br>tensorRT-FP16: 11.16 | 原生：40.01<br>tensorRT-FP32: 36.66  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_imagenet.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/dcn/yolov3_r50vd_dcn.yml) |
+|          YOLOv3 ResNet50_vd DCN          | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) |    42.5    | 50.41 |  原生：16.76<br>tensorRT-FP32: 16.04<br>tensorRT-FP16: 10.70 | 原生：39.64<br>tensorRT-FP32: 35.93  | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_v2.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/dcn/yolov3_r50vd_dcn_obj365_pretrained_coco.yml) |
+|     YOLOv3 ResNet50_vd DCN DropBlock     | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) |     42.8  | 49.97  |  原生：16.55<br>tensorRT-FP32: 16.07<br>tensorRT-FP16: 10.69 | 原生：39.72<br/>tensorRT-FP32: 35.98 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_dropblock.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/dcn/yolov3_r50vd_dcn_db_obj365_pretrained_coco.yml) |
+| YOLOv3 ResNet50_vd DCN DropBlock IoULoss | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) |    43.2    | 49.91 |  原生：16.46<br>tensorRT-FP32: 15.83<br>tensorRT-FP16: 10.80 | 原生：39.58<br/>tensorRT-FP32: 35.61 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_obj365_dropblock_iouloss.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/dcn/yolov3_r50vd_dcn_db_iouloss_obj365_pretrained_coco.yml) |
+| YOLOv3 ResNet50_vd DCN DropBlock IoU-Aware | [Object365 pretrain](https://paddlemodels.bj.bcebos.com/object_detection/ResNet50_vd_dcn_db_obj365_pretrained.tar) |    43.6    | 48.19 |  原生：17.74<br>tensorRT-FP32: 16.73<br>tensorRT-FP16: 11.74 | 原生：41.39<br/>tensorRT-FP32: 37.75 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_r50vd_dcn_db_iouaware_obj365_pretrained_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/dcn/yolov3_r50vd_dcn_db_iouaware_obj365_pretrained_coco.yml) |
 
 <a name="1">[1]</a>V100 python 预测速度是在一张Tesla V100的GPU上通过```tools/eval.py```测试所有验证集得到，单位是fps(图片数/秒), cuDNN版本是7.5，包括数据加载、网络前向执行和后处理, batch size是1。
 

static/docs/featured_model/champion_model/CACascadeRCNN.md

@@ -38,7 +38,7 @@ python tools/train.py -c configs/obj365/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas.y
 
 |        模型         | 验证集 mAP |                           下载链接                           | 配置文件 |
 | :-----------------: | :--------: | :----------------------------------------------------------: | :--------: |
-| CACascadeRCNN SE154 |    31.6    | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas_obj365.tar) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/obj365/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas.yml) |
+| CACascadeRCNN SE154 |    31.6    | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas_obj365.tar) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/obj365/cascade_rcnn_dcnv2_se154_vd_fpn_gn_cas.yml) |
 
 ## 模型效果
 

static/docs/featured_model/champion_model/OIDV5_BASELINE_MODEL.md

@@ -19,15 +19,15 @@ OIDV5模型训练结果如下。
 
 |        模型结构         | Public/Private Score |                           下载链接                           | 配置文件 |
 | :-----------------: | :--------: | :----------------------------------------------------------: | :--------: |
-| CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd |    0.62690/0.59459    | [模型](https://paddlemodels.bj.bcebos.com/object_detection/oidv5_cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/oidv5/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml) |
+| CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd |    0.62690/0.59459    | [模型](https://paddlemodels.bj.bcebos.com/object_detection/oidv5_cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) |  [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/oidv5/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml) |
 
 
 此外，为验证模型的性能，PaddleDetection基于该模型结构，也训练了针对COCO2017和Objects365 Dataset的模型，模型和验证集指标如下表。
 
 |        模型结构         | 数据集 |  验证集mAP |                           下载链接                           | 配置文件 |
 | :-----------------: | :--------: | :--------: | :----------------------------------------------------------: | :--------: |
-| CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd | COCO2017 |    51.7%    | [模型](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/dcn/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml) |
-| CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd | Objects365 |    34.5%    | [模型](https://paddlemodels.bj.bcebos.com/object_detection/obj365_cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/obj365/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml) |
+| CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd | COCO2017 |    51.7%    | [模型](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/dcn/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml) |
+| CascadeCARCNN-FPN-Dcnv2-Nonlocal ResNet200-vd | Objects365 |    34.5%    | [模型](https://paddlemodels.bj.bcebos.com/object_detection/obj365_cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs/obj365/cascade_rcnn_cls_aware_r200_vd_fpn_dcnv2_nonlocal_softnms.yml) |
 
 COCO和Objects365 Dataset数据格式相同，目前只支持预测和评估。
 

static/docs/tutorials/INSTALL.md

@@ -16,7 +16,7 @@ English | [简体中文](INSTALL_cn.md)
 This document covers how to install PaddleDetection, its dependencies
 (including PaddlePaddle), together with COCO and Pascal VOC dataset.
 
-For general information about PaddleDetection, please see [README.md](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/).
+For general information about PaddleDetection, please see [README.md](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/).
 
 
 ## Install PaddlePaddle
@@ -106,7 +106,7 @@ git clone https://github.com/PaddlePaddle/PaddleDetection.git
 
 **Install Python dependencies:**
 
-Required python packages are specified in [requirements.txt](https://github.com/PaddlePaddle/PaddleDetection/blob/master/requirements.txt), and can be installed with:
+Required python packages are specified in [requirements.txt](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/requirements.txt), and can be installed with:
 
 ```
 pip install -r requirements.txt

static/docs/tutorials/INSTALL_cn.md

@@ -13,7 +13,7 @@
 
 这份文档介绍了如何安装PaddleDetection及其依赖项(包括PaddlePaddle)。
 
-PaddleDetection的相关信息，请参考[README.md](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/README_cn.md).
+PaddleDetection的相关信息，请参考[README.md](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/README_cn.md).
 
 
 ## 安装PaddlePaddle
@@ -85,7 +85,7 @@ python -c "import paddle; print(paddle.__version__)"
 
 **安装Python依赖库：**
 
-Python依赖库在[requirements.txt](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/requirements.txt) 中给出，可通过如下命令安装：
+Python依赖库在[requirements.txt](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/requirements.txt) 中给出，可通过如下命令安装：
 
 ```
 pip install -r requirements.txt

static/slim/README.md

@@ -81,7 +81,7 @@
 
 ### 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/release/2.1/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。
 

static/slim/distillation/README.md

@@ -10,7 +10,7 @@
 - [检测库的常规训练方法](https://github.com/PaddlePaddle/PaddleDetection)
 - [PaddleSlim蒸馏API文档](https://paddlepaddle.github.io/PaddleSlim/api/single_distiller_api/)
 
-已发布蒸馏模型见[压缩模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/master/slim/README.md)
+已发布蒸馏模型见[压缩模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/slim/README.md)
 
 ## 安装PaddleSlim
 可按照[PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/)中的步骤安装PaddleSlim
@@ -89,7 +89,7 @@ yolo_output_names = [
 
 ## 训练
 
-根据[PaddleDetection/tools/train.py](https://github.com/PaddlePaddle/PaddleDetection/blob/master/tools/train.py)编写压缩脚本`distill.py`。
+根据[PaddleDetection/tools/train.py](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/tools/train.py)编写压缩脚本`distill.py`。
 在该脚本中定义了teacher_model和student_model，用teacher_model的输出指导student_model的训练
 
 ### 执行示例
@@ -178,7 +178,7 @@ python -u slim/distillation/distill.py \
 
 ## 评估
 
-每隔`snap_shot_iter`步后会保存一个checkpoint模型可以用于评估，使用PaddleDetection目录下[tools/eval.py](https://github.com/PaddlePaddle/PaddleDetection/blob/master/tools/eval.py)评估脚本，并指定`weights`为训练得到的模型路径
+每隔`snap_shot_iter`步后会保存一个checkpoint模型可以用于评估，使用PaddleDetection目录下[tools/eval.py](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/tools/eval.py)评估脚本，并指定`weights`为训练得到的模型路径
 
 运行命令为：
 ```bash
@@ -197,7 +197,7 @@ python -u tools/eval.py -c configs/yolov3_mobilenet_v1.yml \
 
 ## 预测
 
-每隔`snap_shot_iter`步后保存的checkpoint模型也可以用于预测，使用PaddleDetection目录下[tools/infer.py](https://github.com/PaddlePaddle/PaddleDetection/blob/master/tools/infer.py)评估脚本，并指定`weights`为训练得到的模型路径
+每隔`snap_shot_iter`步后保存的checkpoint模型也可以用于预测，使用PaddleDetection目录下[tools/infer.py](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/tools/infer.py)评估脚本，并指定`weights`为训练得到的模型路径
 
 ### Python预测
 

static/slim/nas/README.md

@@ -41,7 +41,7 @@ double_blaze_filters = [
        [self.double_filter_num[tokens[2]], self.mid_filter_num[tokens[6]], self.double_filter_num[tokens[3]], 2],
        [self.double_filter_num[tokens[3]], self.mid_filter_num[tokens[7]], self.double_filter_num[tokens[3]]]]
 ```
-blaze_filters与double_blaze_filters字段请参考[blazenet.py](https://github.com/PaddlePaddle/PaddleDetection/blob/master/ppdet/modeling/backbones/blazenet.py)中定义。
+blaze_filters与double_blaze_filters字段请参考[blazenet.py](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/ppdet/modeling/backbones/blazenet.py)中定义。
 
 初始化tokens为：[2, 1, 3, 8, 2, 1, 2, 1, 1]。
 
@@ -84,10 +84,10 @@ BlazeNet:
 
 - （2）训练、评估与预测：
 
-启动完整的训练评估实验，可参考PaddleDetection的[训练、评估与预测流程](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/tutorials/GETTING_STARTED_cn.md)
+启动完整的训练评估实验，可参考PaddleDetection的[训练、评估与预测流程](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/tutorials/GETTING_STARTED_cn.md)
 
 ## 实验结果
-请参考[人脸检测模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/featured_model/FACE_DETECTION.md)中BlazeFace-NAS的实验结果。
+请参考[人脸检测模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/featured_model/FACE_DETECTION.md)中BlazeFace-NAS的实验结果。
 
 ## FAQ
 - 运行报错：`socket.error: [Errno 98] Address already in use`。

static/slim/prune/README.md

 # 卷积层通道剪裁教程
 
-请确保已正确[安装PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/tutorials/INSTALL_cn.md)及其依赖。
+请确保已正确[安装PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/tutorials/INSTALL_cn.md)及其依赖。
 
 该文档介绍如何使用[PaddleSlim](https://paddlepaddle.github.io/PaddleSlim)的卷积通道剪裁接口对检测库中的模型的卷积层的通道数进行剪裁。
 
@@ -8,15 +8,15 @@
 
 该教程中所示操作，如无特殊说明，均在`PaddleDetection/`路径下执行。
 
-已发布裁剪模型见[压缩模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/master/slim/README.md)
+已发布裁剪模型见[压缩模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/slim/README.md)
 
 ## 1. 数据准备
 
-请参考检测库[数据下载](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/tutorials/INSTALL_cn.md)文档准备数据。
+请参考检测库[数据下载](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/tutorials/INSTALL_cn.md)文档准备数据。
 
 ## 2. 模型选择
 
-通过`-c`选项指定待裁剪模型的配置文件的相对路径，更多可选配置文件请参考: [检测库配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs)
+通过`-c`选项指定待裁剪模型的配置文件的相对路径，更多可选配置文件请参考: [检测库配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.1/static/configs)
 
 对于剪裁任务，原模型的权重不一定对剪裁后的模型训练的重训练有贡献，所以加载原模型的权重不是必需的步骤。
 
@@ -32,7 +32,7 @@
 -o weights=output/yolov3_mobilenet_v1_voc/model_final
 ```
 
-官方已发布的模型请参考: [模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/MODEL_ZOO_cn.md)
+官方已发布的模型请参考: [模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/MODEL_ZOO_cn.md)
 
 ## 3. 确定待分析参数
 
@@ -49,7 +49,7 @@ python slim/prune/prune.py \
 
 ## 4. 分析待剪裁参数敏感度
 
-可通过敏感度分析脚本分析待剪裁参数敏感度得到合适的剪裁率，敏感度分析工具见[敏感度分析](https://github.com/PaddlePaddle/PaddleDetection/blob/master/slim/sensitive/README.md)。
+可通过敏感度分析脚本分析待剪裁参数敏感度得到合适的剪裁率，敏感度分析工具见[敏感度分析](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/slim/sensitive/README.md)。
 
 ## 5. 启动剪裁任务
 
@@ -97,16 +97,16 @@ python slim/prune/export_model.py \
 
 **当前PaddleSlim的剪裁功能不支持剪裁循环体或条件判断语句块内的卷积层，请避免剪裁循环和判断语句块前的一个卷积和语句块内部的卷积。**
 
-对于[faster_rcnn_r50](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/faster_rcnn_r50_1x.yml)或[mask_rcnn_r50](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/mask_rcnn_r50_1x.yml)网络，请剪裁卷积`res4f_branch2c`之前的卷积。
+对于[faster_rcnn_r50](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/faster_rcnn_r50_1x.yml)或[mask_rcnn_r50](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/mask_rcnn_r50_1x.yml)网络，请剪裁卷积`res4f_branch2c`之前的卷积。
 
-对[faster_rcnn_r50](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/faster_rcnn_r50_1x.yml)剪裁示例如下：
+对[faster_rcnn_r50](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/faster_rcnn_r50_1x.yml)剪裁示例如下：
 
 ```
 # demo for faster_rcnn_r50
 python slim/prune/prune.py -c ./configs/faster_rcnn_r50_1x.yml --pruned_params "res4f_branch2b_weights,res4f_branch2a_weights" --pruned_ratios="0.3,0.4" --eval
 ```
 
-对[mask_rcnn_r50](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/mask_rcnn_r50_1x.yml)剪裁示例如下：
+对[mask_rcnn_r50](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs/mask_rcnn_r50_1x.yml)剪裁示例如下：
 
 ```
 # demo for mask_rcnn_r50

static/slim/quantization/README.md

@@ -19,7 +19,7 @@
 
 ## 训练
 
-根据 [tools/train.py](https://github.com/PaddlePaddle/PaddleDetection/blob/master/tools/train.py) 编写压缩脚本train.py。脚本中量化的步骤如下。
+根据 [tools/train.py](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/tools/train.py) 编写压缩脚本train.py。脚本中量化的步骤如下。
 
 ### 定义量化配置
 config = {

static/slim/sensitive/README.md

 # 卷积层敏感度分析教程
 
-请确保已正确[安装PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/tutorials/INSTALL_cn.md)及其依赖。
+请确保已正确[安装PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/tutorials/INSTALL_cn.md)及其依赖。
 
 该文档介绍如何使用[PaddleSlim](https://paddlepaddle.github.io/PaddleSlim)的敏感度分析接口对检测库中的模型的卷积层进行敏感度分析。
 
@@ -10,11 +10,11 @@
 
 ## 数据准备
 
-请参考检测库[数据模块](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/tutorials/INSTALL_cn.md)文档准备数据。
+请参考检测库[数据模块](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/tutorials/INSTALL_cn.md)文档准备数据。
 
 ## 模型选择
 
-通过`-c`选项指定待分析模型的配置文件的相对路径，更多可选配置文件请参考: [检测库配置文件](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs)
+通过`-c`选项指定待分析模型的配置文件的相对路径，更多可选配置文件请参考: [检测库配置文件](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/configs)
 
 通过`-o weights`指定模型的权重，可以指定url或本地文件系统的路径。如下所示：
 
@@ -28,7 +28,7 @@
 -o weights=output/yolov3_mobilenet_v1_voc/model_final
 ```
 
-官方已发布的模型请参考: [模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/master/docs/MODEL_ZOO_cn.md)
+官方已发布的模型请参考: [模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.1/static/docs/MODEL_ZOO_cn.md)
 
 ## 确定待分析参数