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README.md	576cda45b8 first commit	2 年前
README_en.md	576cda45b8 first commit	2 年前
cascade_rcnn_mobilenetv3_fpn_320.yml	576cda45b8 first commit	2 年前
cascade_rcnn_mobilenetv3_fpn_640.yml	576cda45b8 first commit	2 年前
ssdlite_mobilenet_v3_large.yml	576cda45b8 first commit	2 年前
ssdlite_mobilenet_v3_small.yml	576cda45b8 first commit	2 年前
yolov3_mobilenet_v3.yml	576cda45b8 first commit	2 年前
yolov3_reader.yml	576cda45b8 first commit	2 年前

English | 简体中文

移动端模型库

模型

PaddleDetection目前提供一系列针对移动应用进行优化的模型，主要支持以下结构:

骨干网络	结构	输入大小	图片/gpu ¹	学习率策略	Box AP	下载	PaddleLite模型下载
MobileNetV3 Small	SSDLite	320	64	400K (cosine)	16.2	链接	链接
MobileNetV3 Small	SSDLite Quant ²	320	64	400K (cosine)	15.4	链接	链接
MobileNetV3 Large	SSDLite	320	64	400K (cosine)	23.3	链接	链接
MobileNetV3 Large	SSDLite Quant ²	320	64	400K (cosine)	22.6	链接	链接
MobileNetV3 Large w/ FPN	Cascade RCNN	320	2	500k (cosine)	25.0	链接	链接
MobileNetV3 Large w/ FPN	Cascade RCNN	640	2	500k (cosine)	30.2	链接	链接
MobileNetV3 Large	YOLOv3	320	8	500K	27.1	链接	链接
MobileNetV3 Large	YOLOv3 Prune ³	320	8	-	24.6	链接	链接

注意:

[1] 模型统一使用8卡训练。
[2] 参考下面关于SSDLite量化的说明。
[3] 参考下面关于YOLO剪裁的说明。

评测结果

模型使用 Paddle-Lite 2.6 (即将发布) 在下列平台上进行了测试
- Qualcomm Snapdragon 625
- Qualcomm Snapdragon 835
- Qualcomm Snapdragon 845
- Qualcomm Snapdragon 855
- HiSilicon Kirin 970
- HiSilicon Kirin 980
单CPU线程 (单位： ms)

	SD625	SD835	SD845	SD855	Kirin 970	Kirin 980
SSDLite Large	289.071	134.408	91.933	48.2206	144.914	55.1186
SSDLite Large Quant
SSDLite Small	122.932	57.1914	41.003	22.0694	61.5468	25.2106
SSDLite Small Quant
YOLOv3 baseline	1082.5	435.77	317.189	155.948	536.987	178.999
YOLOv3 prune	253.98	131.279	89.4124	48.2856	122.732	55.8626
Cascade RCNN 320	286.526	125.635	87.404	46.184	149.179	52.9994
Cascade RCNN 640	1115.66	495.926	351.361	189.722	573.558	207.917

4 CPU线程 (单位： ms)

	SD625	SD835	SD845	SD855	Kirin 970	Kirin 980
SSDLite Large	107.535	51.1382	34.6392	20.4978	50.5598	24.5318
SSDLite Large Quant
SSDLite Small	51.5704	24.5156	18.5486	11.4218	24.9946	16.7158
SSDLite Small Quant
YOLOv3 baseline	413.486	184.248	133.624	75.7354	202.263	126.435
YOLOv3 prune	98.5472	53.6228	34.4306	21.3112	44.0722	31.201
Cascade RCNN 320	131.515	59.6026	39.4338	23.5802	58.5046	36.9486
Cascade RCNN 640	473.083	224.543	156.205	100.686	231.108	138.391

SSDLite量化说明

在SSDLite模型中我们采用完整量化训练的方式对模型进行训练，在8卡GPU下共训练40万轮，训练中将res_conv1与se_block固定不训练，执行指令为：

python slim/quantization/train.py --not_quant_pattern res_conv1 se_block \
        -c configs/ssd/ssdlite_mobilenet_v3_large.yml \
        --eval

更多量化教程请参考模型量化压缩教程

YOLOv3剪裁说明

首先对YOLO检测头进行剪裁，然后再使用 YOLOv3-ResNet34 作为teacher网络对剪裁后的模型进行蒸馏, teacher网络在COCO上的mAP为31.4 (输入大小320*320).

可以使用如下两种方式进行剪裁:

固定比例剪裁, 整体剪裁率是86%

--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" \
--pruned_ratios="0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.75,0.875,0.875,0.875,0.875,0.875,0.875"

使用 FPGM 算法剪裁:

--prune_criterion=geometry_median

敬请关注后续发布

更多模型
量化模型

README.md

移动端模型库

模型

评测结果

SSDLite量化说明

YOLOv3剪裁说明

敬请关注后续发布