From ad04a4377cb1dfdb41728b20e52da703e8c3cb67 Mon Sep 17 00:00:00 2001 From: Zheng_Bicheng <58363586+Zheng-Bicheng@users.noreply.github.com> Date: Wed, 9 Nov 2022 13:57:02 +0800 Subject: [PATCH] [Doc]Fix doc error (#539) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 修正RKPicodet文档 * 修正yolov5文档模型大小的错误 --- .../detection/paddledetection/rknpu2/README.md | 16 ++++++++-------- examples/vision/detection/yolov5/README.md | 10 +++++----- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/examples/vision/detection/paddledetection/rknpu2/README.md b/examples/vision/detection/paddledetection/rknpu2/README.md index 32eff20a6f..8b15a7d3e9 100644 --- a/examples/vision/detection/paddledetection/rknpu2/README.md +++ b/examples/vision/detection/paddledetection/rknpu2/README.md @@ -15,23 +15,23 @@ RKNPU部署模型前需要将Paddle模型转换成RKNN模型，具体步骤如 ## 模型转换example 下面以Picodet-npu为例子,教大家如何转换PaddleDetection模型到RKNN模型。 ```bash -## 下载Paddle静态图模型并解压 -wget https://bj.bcebos.com/fastdeploy/models/rknn2/picodet_s_416_coco_npu.zip -unzip -qo picodet_s_416_coco_npu.zip +# 下载Paddle静态图模型并解压 +wget https://paddledet.bj.bcebos.com/deploy/Inference/picodet_s_416_coco_lcnet.tar +tar xvf picodet_s_416_coco_lcnet.zip # 静态图转ONNX模型，注意，这里的save_file请和压缩包名对齐 -paddle2onnx --model_dir picodet_s_416_coco_npu \ +paddle2onnx --model_dir picodet_s_416_coco_lcnet \ --model_filename model.pdmodel \ --params_filename model.pdiparams \ - --save_file picodet_s_416_coco_npu/picodet_s_416_coco_npu.onnx \ + --save_file picodet_s_416_coco_lcnet/picodet_s_416_coco_lcnet.onnx \ --enable_dev_version True -python -m paddle2onnx.optimize --input_model picodet_s_416_coco_npu/picodet_s_416_coco_npu.onnx \ - --output_model picodet_s_416_coco_npu/picodet_s_416_coco_npu.onnx \ +python -m paddle2onnx.optimize --input_model picodet_s_416_coco_lcnet/picodet_s_416_coco_lcnet.onnx \ + --output_model picodet_s_416_coco_lcnet/picodet_s_416_coco_lcnet.onnx \ --input_shape_dict "{'image':[1,3,416,416]}" # ONNX模型转RKNN模型 # 转换模型,模型将生成在picodet_s_320_coco_lcnet_non_postprocess目录下 -python tools/rknpu2/export.py --config_path tools/rknpu2/config/RK3588/picodet_s_416_coco_npu.yaml +python tools/rknpu2/export.py --config_path tools/rknpu2/config/RK3588/picodet_s_416_coco_lcnet.yaml ``` - [Python部署](./python) diff --git a/examples/vision/detection/yolov5/README.md b/examples/vision/detection/yolov5/README.md index 222cb53f7e..076ae01ce6 100644 --- a/examples/vision/detection/yolov5/README.md +++ b/examples/vision/detection/yolov5/README.md @@ -10,11 +10,11 @@ 为了方便开发者的测试，下面提供了YOLOv5导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库） | 模型 | 大小 | 精度 | |:---------------------------------------------------------------- |:----- |:----- | -| [YOLOv5n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n.onnx) | 1.9MB | 28.4% | -| [YOLOv5s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx) | 7.2MB | 37.2% | -| [YOLOv5m](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5m.onnx) | 21.2MB | 45.2% | -| [YOLOv5l](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5l.onnx) | 46.5MB | 48.8% | -| [YOLOv5x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5x.onnx) | 86.7MB | 50.7% | +| [YOLOv5n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n.onnx) | 7.5MB | 28.4% | +| [YOLOv5s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx) | 28.9MB | 37.2% | +| [YOLOv5m](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5m.onnx) | 84.7MB | 45.2% | +| [YOLOv5l](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5l.onnx) | 186.2MB | 48.8% | +| [YOLOv5x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5x.onnx) | 346.9MB | 50.7% |