# Practical Mobile-side detection method base on RCNN## Introduction* This is developed by PaddleDetection. Many useful tricks are utilized for the model training process. More details can be seen in the configuration file.* The inerence is tested on Qualcomm Snapdragon 845 Mobile Platform.## Model Zoo| Backbone | Type | Image/gpu | Lr schd | Inf time on SD845 (fps) | Box AP | Mask AP | Download || :---------------------- | :-------------: | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: || MobileNetV3-vd-FPN | Cascade Faster | 2 | 5.6x(CosineDecay) | 8.13 | 25.0 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_320.tar) || MobileNetV3-vd-FPN | Cascade Faster | 2 | 5.6x(CosineDecay) | 2.66 | 30.2 | - | [model](https://paddlemodels.bj.bcebos.com/object_detection/cascade_rcnn_mobilenetv3_fpn_640.tar) |**note***`5.6x` means the model is trained with `50000` minibatches 8 GPU cards(batch size=2 for each card).
