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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。
-
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 |
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
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
- 更多模型
- 量化模型