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README.md

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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:

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