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未验证 提交 97088feb 编写于 作者: K Kaipeng Deng 提交者： GitHub
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add slim model zoo (#193) 

* add slim model zoo
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# 压缩模型库
## 测试环境
- Python 2.7.1
- PaddlePaddle >=1.6
- CUDA 9.0
- cuDNN >=7.4
- NCCL 2.1.2
## 裁剪模型库
### 训练策略
- 裁剪模型训练时使用[PaddleDetection模型库](../../docs/MODEL_ZOO_cn.md)发布的模型权重作为预训练权重。
- 裁剪训练使用模型默认配置，即除`pretrained_weights`外配置不变。
- 裁剪模型全部为基于敏感度的卷积通道裁剪。
- YOLOv3模型主要裁剪`yolo_head`部分，即裁剪参数如下。
```
--pruned_params="yolo_block.0.0.0.conv.weights,yolo_block.0.0.1.conv.weights,yolo_block.0.1.0.conv.weights,yolo_block.0.1.1.conv.weights,yolo_block.0.2.conv.weights,yolo_block.0.tip.conv.weights,yolo_block.1.0.0.conv.weights,yolo_block.1.0.1.conv.weights,yolo_block.1.1.0.conv.weights,yolo_block.1.1.1.conv.weights,yolo_block.1.2.conv.weights,yolo_block.1.tip.conv.weights,yolo_block.2.0.0.conv.weights,yolo_block.2.0.1.conv.weights,yolo_block.2.1.0.conv.weights,yolo_block.2.1.1.conv.weights,yolo_block.2.2.conv.weights,yolo_block.2.tip.conv.weights"
```
- YOLOv3模型裁剪中裁剪策略`r578`表示`yolo_head`中三个输出分支一次使用`0.5, 0.7, 0.8`的裁剪率裁剪，即裁剪率如下。
```
--pruned_ratios="0.5,0.5,0.5,0.5,0.5,0.5,0.7,0.7,0.7,0.7,0.7,0.7,0.8,0.8,0.8,0.8,0.8,0.8"
```
- YOLOv3模型裁剪中裁剪策略`sensity`表示`yolo_head`中各参数裁剪率如下，该裁剪率为使用`yolov3_mobilnet_v1`模型在COCO数据集上敏感度实验分析得出。
```
--pruned_ratios="0.1,0.2,0.2,0.2,0.2,0.1,0.2,0.3,0.3,0.3,0.2,0.1,0.3,0.4,0.4,0.4,0.4,0.3"
```
### YOLOv3 on COCO
| 骨架网络 | 裁剪策略 | 输入尺寸 | Box AP | 下载 |
| :----------------| :-------: | :------: |:------: | :-----------------------------------------------------: |
| ResNet50-vd-dcn | sensity | 320 | 39.8 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_r50_dcn_prune1x.tar) |
| MobileNetV1 | r578 | 608 | 27.8 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_prune578.tar) |
| MobileNetV1 | r578 | 416 | 26.8 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_prune578.tar) |
| MobileNetV1 | r578 | 320 | 24.0 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_prune578.tar) |
### YOLOv3 on Pascal VOC
| 骨架网络 | 裁剪策略 | 输入尺寸 | Box AP | 下载 |
| :----------------| :-------: | :------: |:------: | :-----------------------------------------------------: |
| MobileNetV1 | r578 | 608 | 77.6 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_voc_prune578.tar) |
| MobileNetV1 | r578 | 416 | 77.7 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_voc_prune578.tar) |
| MobileNetV1 | r578 | 320 | 75.5 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_voc_prune578.tar) |
## 蒸馏模型库
### 训练策略
- 蒸馏模型训练时teacher模型使用[PaddleDetection模型库](../../docs/MODEL_ZOO_cn.md)发布的模型权重作为预训练权重。
- 蒸馏模型训练时student模型使用backbone的预训练权重
### YOLOv3 on COCO
| 骨架网络 | 蒸馏策略 | 输入尺寸 | Box AP | 下载 |
| :----------------| :-----------: | :------: |:------: | :-----------------------------------------------------: |
| MobileNetV1 | split_distiil | 608 | 31.4 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_distilled.tar) |
| MobileNetV1 | split_distiil | 416 | 30.0 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_distilled.tar) |
| MobileNetV1 | split_distiil | 320 | 27.1 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_distilled.tar) |
### YOLOv3 on Pascal VOC
| 骨架网络 | 蒸馏策略 | 输入尺寸 | Box AP | 下载 |
| :----------------| :-----------: | :------: |:------: | :-----------------------------------------------------: |
| MobileNetV1 | l2_distiil | 608 | 79.0 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_voc_distilled.tar) |
| MobileNetV1 | l2_distiil | 416 | 78.2 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_voc_distilled.tar) |
| MobileNetV1 | l2_distiil | 320 | 75.5 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_voc_distilled.tar) |
## 量化模型库
### 训练策略
- 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。
### YOLOv3 on COCO
| 骨架网络 | 预训练权重 | 量化策略 | 输入尺寸 | Box AP | 下载 |
| :----------------| :--------: | :------: | :------: |:------: | :-----------------------------------------------------: |
| MobileNetV1 | ImageNet | post | 608 | 27.9 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) |
| MobileNetV1 | ImageNet | post | 416 | 28.0 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) |
| MobileNetV1 | ImageNet | post | 320 | 26.0 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_post.tar) |
| MobileNetV1 | ImageNet | aware | 608 | 28.1 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_aware.tar) |
| MobileNetV1 | ImageNet | aware | 416 | 28.2 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_aware.tar) |
| MobileNetV1 | ImageNet | aware | 320 | 25.8 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_mobilenetv1_coco_quant_aware.tar) |
| ResNet34 | ImageNet | post | 608 | 35.7 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_post.tar) |
| ResNet34 | ImageNet | aware | 608 | 35.2 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) |
| ResNet34 | ImageNet | aware | 416 | 33.3 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) |
| ResNet34 | ImageNet | aware | 320 | 30.3 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/yolov3_r34_coco_quant_aware.tar) |
| 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) |
| 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) |
| 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) |
### BlazeFace on WIDER FACE
| 模型 | 量化策略 | 输入尺寸 | Easy Set | Medium Set | Hard Set | 下载 |
| :--------------- | :------: | :------: | :------: | :--------: | :------: | :-----------------------------------------------------: |
| BlazeFace | post | 640 | 87.8 | 85.1 | 74.9 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_origin_quant_post.tar) |
| BlazeFace | aware | 640 | 90.5 | 87.9 | 77.6 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_origin_quant_aware.tar) |
| BlazeFace-Lite | post | 640 | 89.4 | 86.7 | 75.7 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_lite_quant_post.tar) |
| BlazeFace-Lite | aware | 640 | 89.7 | 87.3 | 77.0 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_lite_quant_aware.tar) |
| BlazeFace-NAS | post | 640 | 81.6 | 78.3 | 63.6 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_nas_quant_post.tar) |
| BlazeFace-NAS | aware | 640 | 83.1 | 79.7 | 64.2 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/blazeface_nas_quant_aware.tar) |
slim/distillation/README.md
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- [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](../MODEL_ZOO.md)
## 安装PaddleSlim
可按照[PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/)中的步骤安装PaddleSlim
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slim/prune/README.md
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该教程中所示操作，如无特殊说明，均在`PaddleDetection/slim/prune/`路径下执行。
已发布裁剪模型见[压缩模型库](../MODEL_ZOO.md)
## 1. 数据准备
请参考检测库[数据下载](../../docs/INSTALL_cn.md)文档准备数据。
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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：
```
-o weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar
-o weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar
```
或
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通过以下命令查看当前模型的所有参数：
```
python prune.py \
python prune.py \
-c ../../configs/yolov3_mobilenet_v1_voc.yml \
--print_params
```
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slim/quantization/README.md
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- [检测模型的常规训练方法](https://github.com/PaddlePaddle/PaddleDetection)
- [PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/)
已发布量化模型见[压缩模型库](../MODEL_ZOO.md)
## 安装PaddleSlim
可按照[PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/)中的步骤安装PaddleSlim。
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