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PaddleDetection
未验证 提交 8055e86e 编写于 作者: K Kaipeng Deng 提交者： GitHub
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split weights and PaddleLite model in configs/mobile/README (#935) 

* split weights and PaddleLite model in configs/mobile/README
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configs/mobile/README.md
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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 <sup>1</sup> | 学习率策略 | Box AP | 下载 <sup>2</sup> |
|--------------------------|---------------------------|-------|------------------------|---------------|--------|-----------------------|
| MobileNetV3 Small | SSDLite | 320 | 64 | 400K (cosine) | 16.6 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v3_small.tar.gz) |
| MobileNetV3 Large | SSDLite | 320 | 64 | 400K (cosine) | 22.8 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v3_large.tar.gz) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 320 | 2 | 500k (cosine) | 25.0 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_320.tar.gz) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 640 | 2 | 500k (cosine) | 30.2 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_640.tar.gz) |
| MobileNetV3 Large | YOLOv3 | 320 | 8 | 500K | 27.1 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.tar.gz) |
| MobileNetV3 Large | YOLOv3 Prune <sup>3</sup> | 320 | 8 | - | 24.6 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3_prune86_FPGM_320.tar.gz) |
| 骨干网络 | 结构 | 输入大小 | 图片/gpu <sup>1</sup> | 学习率策略 | Box AP | 下载 | PaddleLite模型下载 |
| :----------------------- | :------------------------ | :---: | :--------------------: | :------------ | :----: | :--- | :----------------- |
| MobileNetV3 Small | SSDLite | 320 | 64 | 400K (cosine) | 16.6 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small.tar) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_small.tar) |
| MobileNetV3 Large | SSDLite | 320 | 64 | 400K (cosine) | 22.8 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large.tar) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_large.tar) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 320 | 2 | 500k (cosine) | 25.0 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/cascade_rcnn_mobilenetv3_fpn_320.tar) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/cascade_rcnn_mobilenetv3_fpn_320.tar) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 640 | 2 | 500k (cosine) | 30.2 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/cascade_rcnn_mobilenetv3_fpn_640.tar) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/cascade_rcnn_mobilenetv3_fpn_640.tar) |
| MobileNetV3 Large | YOLOv3 | 320 | 8 | 500K | 27.1 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.pdparams) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/yolov3_mobilenet_v3.tar) |
| MobileNetV3 Large | YOLOv3 Prune <sup>2</sup> | 320 | 8 | - | 24.6 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/yolov3_mobilenet_v3_prune75875_FPGM_distillby_r34.pdparams) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/yolov3_mobilenet_v3_prune86_FPGM_320.tar) |
**注意**:
- <a name="gpu">[1]</a> 模型统一使用8卡训练.
- <a name="tarball">[2]</a> 压缩包包括下列文件
- 模型权重文件 (`.pdparams` or `.tar`)
- inference model 文件 (`__model__` and `__params__`)
- Paddle-Lite 模型文件 (`.nb`)
- <a name="prune">[3]</a> 参考下面关于YOLO剪裁的说明
- <a name="prune">[2]</a> 参考下面关于YOLO剪裁的说明
## 评测结果
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configs/mobile/README_en.md
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This directory contains models optimized for mobile applications, at present the following models included:
| Backbone | Architecture | Input | Image/gpu <sup>1</sup> | Lr schd | Box AP | Download <sup>2</sup> |
|--------------------------|---------------------------|-------|------------------------|---------------|--------|-----------------------|
| MobileNetV3 Small | SSDLite | 320 | 64 | 400K (cosine) | 16.6 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v3_small.tar.gz) |
| MobileNetV3 Large | SSDLite | 320 | 64 | 400K (cosine) | 22.8 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v3_large.tar.gz) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 320 | 2 | 500k (cosine) | 25.0 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_320.tar.gz) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 640 | 2 | 500k (cosine) | 30.2 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_640.tar.gz) |
| MobileNetV3 Large | YOLOv3 | 320 | 8 | 500K | 27.1 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.tar.gz) |
| MobileNetV3 Large | YOLOv3 Prune <sup>3</sup> | 320 | 8 | - | 24.6 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3_prune86_FPGM_320.tar.gz) |
| Backbone | Architecture | Input | Image/gpu <sup>1</sup> | Lr schd | Box AP | Download | PaddleLite Model Download |
| :----------------------- | :------------------------ | :---: | :--------------------: | :------------ | :----: | :------- | :------------------------ |
| MobileNetV3 Small | SSDLite | 320 | 64 | 400K (cosine) | 16.6 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small.tar) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_small.tar) |
| MobileNetV3 Large | SSDLite | 320 | 64 | 400K (cosine) | 22.8 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large.tar) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_large.tar) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 320 | 2 | 500k (cosine) | 25.0 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/cascade_rcnn_mobilenetv3_fpn_320.tar) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/cascade_rcnn_mobilenetv3_fpn_320.tar) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 640 | 2 | 500k (cosine) | 30.2 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/cascade_rcnn_mobilenetv3_fpn_640.tar) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/cascade_rcnn_mobilenetv3_fpn_640.tar) |
| MobileNetV3 Large | YOLOv3 | 320 | 8 | 500K | 27.1 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.pdparams) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/yolov3_mobilenet_v3.tar) |
| MobileNetV3 Large | YOLOv3 Prune <sup>2</sup> | 320 | 8 | - | 24.6 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/yolov3_mobilenet_v3_prune75875_FPGM_distillby_r34.pdparams) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/yolov3_mobilenet_v3_prune86_FPGM_320.tar) |
**Notes**:
- <a name="gpu">[1]</a> All models are trained on 8 GPUs.
- <a name="tarball">[2]</a> Each tarball contains the following files
- model weight file (`.pdparams` or `.tar`)
- inference model files (`__model__` and `__params__`)
- Paddle-Lite model file (`.nb`)
- <a name="prune">[3]</a> See the note section on how YOLO head is pruned
- <a name="prune">[2]</a> See the note section on how YOLO head is pruned
## Benchmarks Results
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