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Add links to tutorials of paddledetection. (PaddlePaddle#156)
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‎docs/zh_cn/tutorials/index.rst

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image_classification_sensitivity_analysis_tutorial.md
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darts_nas_turorial.md
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paddledetection_slim_distillation_tutorial.md
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paddledetection_slim_nas_tutorial.md
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paddledetection_slim_pruing_tutorial.md
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paddledetection_slim_prune_dist_tutorial.md
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paddledetection_slim_quantization_tutorial.md
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paddledetection_slim_sensitivy_tutorial.md

‎docs/zh_cn/tutorials/paddledetection_slim_distillation_tutorial.md

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# 目标检测模型蒸馏教程
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教程内容请参考：https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/slim/distillation/README.md
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## 示例结果
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### MobileNetV1-YOLO-V3-VOC
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| FLOPS |输入尺寸|每张GPU图片个数|推理时间（fps）|Box AP|下载|
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|:-:|:-:|:-:|:-:|:-:|:-:|
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|baseline|608 |16|104.291|76.2|[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar)|
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|蒸馏后|608 |16|106.914|79.0|[下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_voc_distilled.tar)|
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|baseline|416 |16|-|76.7|[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar)|
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|蒸馏后|416 |16|-|78.2|[下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_voc_distilled.tar)|
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|baseline|320 |16|-|75.3|[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar)|
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|蒸馏后|320 |16|-|75.5|[下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_voc_distilled.tar)|
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> 蒸馏后的结果用ResNet34-YOLO-V3做teacher，4GPU总batch_size64训练90000 iter得到
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### MobileNetV1-YOLO-V3-COCO
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| FLOPS |输入尺寸|每张GPU图片个数|推理时间（fps）|Box AP|下载|
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|:-:|:-:|:-:|:-:|:-:|:-:|
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|baseline|608 |16|78.302|29.3|[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar)|
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|蒸馏后|608 |16|78.523|31.4|[下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_distilled.tar)|
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|baseline|416 |16|-|29.3|[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar)|
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|蒸馏后|416 |16|-|30.0|[下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_distilled.tar)|
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|baseline|320 |16|-|27.0|[下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar)|
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|蒸馏后|320 |16|-|27.1|[下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_distilled.tar)|
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> 蒸馏后的结果用ResNet34-YOLO-V3做teacher，4GPU总batch_size64训练600000 iter得到

‎docs/zh_cn/tutorials/paddledetection_slim_nas_tutorial.md

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# 人脸检测模型小模型结构搜索教程
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教程内容请参考：https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/slim/nas/README.md
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## 概述
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我们选取人脸检测的BlazeFace模型作为神经网络搜索示例，该示例使用PaddleSlim 辅助完成神经网络搜索实验。
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基于PaddleSlim进行搜索实验过程中，搜索限制条件可以选择是浮点运算数(FLOPs)限制还是硬件延时(latency)限制，硬件延时限制需要提供延时表。本示例提供一份基于blazeface搜索空间的硬件延时表，名称是latency_855.txt(基于PaddleLite在骁龙855上测试的延时)，可以直接用该表进行blazeface的硬件延时搜索实验。

‎docs/zh_cn/tutorials/paddledetection_slim_pruing_tutorial.md

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# 目标检测模型卷积通道剪裁教程
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请参考：https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/slim/prune/README.md

‎docs/zh_cn/tutorials/paddledetection_slim_prune_dist_tutorial.md

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# 目标检测模型蒸馏剪裁教程
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教程内容请参考：https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/slim/extensions/distill_pruned_model/README.md
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## 概述
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该文档介绍如何使用PaddleSlim的蒸馏接口和卷积通道剪裁接口对检测库中的模型进行卷积层的通道剪裁并使用较高精度模型对其蒸馏。

‎docs/zh_cn/tutorials/paddledetection_slim_quantization_tutorial.md

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# 目标检测模型定点量化教程
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教程内容请参考：https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/slim/quantization/README.md
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## 示例结果
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### 训练策略
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- 量化策略`post`为使用离线量化得到的模型，`aware`为在线量化训练得到的模型。
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### YOLOv3 on COCO
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| 骨架网络 | 预训练权重 | 量化策略 | 输入尺寸 | Box AP | 下载 |
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| :----------------| :--------: | :------: | :------: |:------: | :-----------------------------------------------------: |
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| MobileNetV1 | ImageNet | post | 608 | 27.9 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) |
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| MobileNetV1 | ImageNet | post | 416 | 28.0 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) |
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| MobileNetV1 | ImageNet | post | 320 | 26.0 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) |
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| MobileNetV1 | ImageNet | aware | 608 | 28.1 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_aware.tar) |
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| MobileNetV1 | ImageNet | aware | 416 | 28.2 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_aware.tar) |
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| MobileNetV1 | ImageNet | aware | 320 | 25.8 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_aware.tar) |
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| ResNet34 | ImageNet | post | 608 | 35.7 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_post.tar) |
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| ResNet34 | ImageNet | aware | 608 | 35.2 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) |
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| ResNet34 | ImageNet | aware | 416 | 33.3 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) |
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| ResNet34 | ImageNet | aware | 320 | 30.3 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) |
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| R50vd-dcn | object365 | aware | 608 | 40.6 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_obj365_pretrained_coco_quant_aware.tar) |
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| R50vd-dcn | object365 | aware | 416 | 37.5 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_obj365_pretrained_coco_quant_aware.tar) |
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| R50vd-dcn | object365 | aware | 320 | 34.1 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r50vd_dcn_obj365_pretrained_coco_quant_aware.tar) |

‎docs/zh_cn/tutorials/paddledetection_slim_sensitivy_tutorial.md

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# 目标检测模型敏感度分析教程
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教程内容请参考：https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.2/slim/sensitive/README.md

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