From f5d7980049c8d42008c7bf238e93f3329b2da040 Mon Sep 17 00:00:00 2001 From: Yang Zhang Date: Thu, 14 May 2020 10:54:59 +0800 Subject: [PATCH] Remove leftover mobile doc (#661) --- docs/mobile_side/README.md | 33 --------------------------------- 1 file changed, 33 deletions(-) delete mode 100644 docs/mobile_side/README.md diff --git a/docs/mobile_side/README.md b/docs/mobile_side/README.md deleted file mode 100644 index 5db744768..000000000 --- a/docs/mobile_side/README.md +++ /dev/null @@ -1,33 +0,0 @@ -# Practical Mobile-side detection method base on PaddleDetection - -Mobile-side models are provided base on following architecture: - -1. YOLOv3 -2. Cascade Faster RCNN -3. SSD - -## YOLOv3 mobile-side model - -Mobile-side model based on YOLOv3 is a pruned model of YOLOv3-MobileNetv3, we pruned the YOLO-head of YOLOv3-MobileNetv3 and distill the pruned model by YOLOv3-ResNet34, which has a higher mAP on COCO as 31.4(input shape as 320\*320). For pruning, configurations as as follows: - -1. pruning YOLO-head with following configuration and the FLOPS pruned ratios is 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" -``` - -2. pruning YOLO-head by [Filter Pruning via Geometric Median](https://arxiv.org/abs/1811.00250), use FPGM algorithm by setting: - -``` ---prune_criterion=geometry_median -``` - -### Model Zoo - -| Backbone | prune method | GFLOPs | Model size(MB) | input shape | teacher model | Box AP | SD845 latency | download | -| :----------------| :----------: | :-----------: | :------------: | :---------: | :-------------------: | :--------: | :-----------: |:-----------------------------------------------------: | -| MobileNetv3 | baseline | 4.93 | 90.23 | 320 | - | 27.1 | 319ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.pdparams) | -| MobileNetv3 | prune | 0.66(-86.57%) | 16.21(-82.03%) | 320 | YOLOv3-ResNet34(31.4) | 24.6(-2.5) | 91ms | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3_mobilenetv3_prune75875_FPGM_distillby_r34.pdparams) | - -**NOTE：** `baseline` is the YOLOv3-MobileNetv3 base model, `prune` is the pruned model from YOLOv3-MobileNetv3 and pruned by configurations above, `Box AP` is test by `320*320` as input shape in both two models, and `latency` is test on Snapdragon845 with single thread. The pruned model is 2.5 times faster than the base model when the `Box AP` only decreased by 2.5. -- GitLab