diff --git a/README.md b/README.md index 7351e277220eff5a9626bccccf8234ae8937f5d8..a19efa5681c102984ed14f1cc02fbf3eb49a351d 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ PaddleDetection的目的是为工业界和学术界提供丰富、易用的目标检测模型。不仅性能优越、易于部署,而且能够灵活的满足算法研究的需求。 -**目前检测库下模型均要求使用PaddlePaddle 1.6及以上版本或适当的develop版本。** +**目前检测库下模型均要求使用PaddlePaddle 1.7及以上版本或适当的develop版本。**
diff --git a/slim/README.md b/slim/README.md index 237b11a6b9255aee4ef3cd5f9cfbf3432d0fb9b8..bc63703ef464bbe4728536f6795574d23bb6f75c 100644 --- a/slim/README.md +++ b/slim/README.md @@ -86,6 +86,30 @@ Pascal VOC数据集上蒸馏通道剪裁模型库如下。 | MobileNetV1 | r578 | 69.57% | 67.00% | 416 | YOLOv3-ResNet34 | 78.7 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_voc_prune578_distillby_r34.tar) | | MobileNetV1 | r578 | 69.57% | 67.00% | 320 | YOLOv3-ResNet34 | 76.3 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_voc_prune578_distillby_r34.tar) | +### YOLOv3通道剪裁模型推理时延 + +- 时延单位均为`ms/images` +- Tesla P4时延为单卡并开启TensorRT推理时延 +- 高通835/高通855/麒麟970时延为使用PaddleLite部署,使用`arm8`架构并使用4线程(4 Threads)推理时延 + +| 骨架网络 | 数据集 | 剪裁策略 | FLOPs剪裁率 | 模型体积剪裁率 | 输入尺寸 | Tesla P4 | 麒麟970 | 高通835 | 高通855 | +| :--------------- | :----: | :------: | :---------: | :------------: | :------: | :------: | :-----: | :-----: | :-----: | +| MobileNetV1 | VOC | baseline | - | - | 608 | 16.556 | 748.404 | 734.970 | 289.878 | +| MobileNetV1 | VOC | baseline | - | - | 416 | 9.031 | 371.214 | 349.065 | 140.877 | +| MobileNetV1 | VOC | baseline | - | - | 320 | 6.235 | 221.705 | 200.498 | 80.515 | +| MobileNetV1 | VOC | r578 | 69.57% | 67.00% | 608 | 10.064 | 314.531 | 323.537 | 123.414 | +| MobileNetV1 | VOC | r578 | 69.57% | 67.00% | 416 | 5.478 | 151.562 | 146.014 | 56.420 | +| MobileNetV1 | VOC | r578 | 69.57% | 67.00% | 320 | 3.880 | 91.132 | 87.440 | 31.470 | +| ResNet50-vd-dcn | COCO | baseline | - | - | 608 | 36.127 | - | - | - | +| ResNet50-vd-dcn | COCO | baseline | - | - | 416 | 20.437 | - | - | - | +| ResNet50-vd-dcn | COCO | baseline | - | - | 320 | 14.037 | - | - | - | +| ResNet50-vd-dcn | COCO | sensity | 18.41% | 15.46% | 608 | 33.245 | - | - | - | +| ResNet50-vd-dcn | COCO | sensity | 18.41% | 15.46% | 416 | 19.246 | - | - | - | +| ResNet50-vd-dcn | COCO | sensity | 18.41% | 15.46% | 320 | 13.656 | - | - | - | +| ResNet50-vd-dcn | COCO | r578 | 43.69% | 36.61% | 608 | 29.138 | - | - | - | +| ResNet50-vd-dcn | COCO | r578 | 43.69% | 36.61% | 416 | 16.439 | - | - | - | +| ResNet50-vd-dcn | COCO | r578 | 43.69% | 36.61% | 320 | 11.339 | - | - | - | + ## 蒸馏模型库 diff --git a/slim/quantization/README.md b/slim/quantization/README.md index 960eac4c60ed3f329ea8796f5e88e1ccf45444a3..48644c1de708e5a84a5ed040abe5144eb7940e77 100644 --- a/slim/quantization/README.md +++ b/slim/quantization/README.md @@ -63,7 +63,7 @@ python slim/quantization/train.py --not_quant_pattern yolo_output \ -o max_iters=30000 \ save_dir=./output/mobilenetv1 \ LearningRate.base_lr=0.0001 \ - LearningRate.schedulers='[!PiecewiseDecay {gamma: 0.1, milestones: [10000]}]' \ + LearningRate.schedulers="[!PiecewiseDecay {gamma: 0.1, milestones: [10000]}]" \ pretrain_weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar ```