diff --git a/slim/MODEL_ZOO.md b/slim/MODEL_ZOO.md new file mode 100644 index 0000000000000000000000000000000000000000..116b7de8075135e22de9245ce2a48bc9369f9038 --- /dev/null +++ b/slim/MODEL_ZOO.md @@ -0,0 +1,109 @@ +# 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) | diff --git a/slim/distillation/README.md b/slim/distillation/README.md index e1947bdd51497b94e6f8c38b58668f9cbf73b41e..b2ecbb6bc4bba41964e0ee75581b2ca8410e04d2 100644 --- a/slim/distillation/README.md +++ b/slim/distillation/README.md @@ -10,6 +10,8 @@ - [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 diff --git a/slim/prune/README.md b/slim/prune/README.md index 31e537d5339dc6d883b9c4870591b6bec95d37c3..a3505545e50cf6e8735bb1bf17eeddb970bc303a 100644 --- a/slim/prune/README.md +++ b/slim/prune/README.md @@ -8,6 +8,8 @@ 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。 @@ -21,7 +23,7 @@ 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 @@ -38,7 +40,7 @@ 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 ``` diff --git a/slim/quantization/README.md b/slim/quantization/README.md index fb002eb4b9eab28ffa05b0bd239d003c68238461..85c52a130f072545f7d3372ccde835d5a02dfb8b 100644 --- a/slim/quantization/README.md +++ b/slim/quantization/README.md @@ -10,6 +10,7 @@ - [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。