From 048300ebf31cdb4a4b8b3bea81d3d1d3840445bf Mon Sep 17 00:00:00 2001 From: Feng Ni Date: Mon, 14 Nov 2022 13:32:48 +0800 Subject: [PATCH] [ppyoloe-sod] remove smalldet cfgs and fix doc (#7312) * remove duplicate backbone variant, test=document_fix * remove smalldet cfgs and fix doc * add speed test doc and deploy model links * fix doc * add smalldet demo jpg --- configs/smalldet/README.md | 62 ++++++-- configs/smalldet/visdrone/README.md | 143 ++++++++++++++++++ .../visdrone/ppyoloe_crn_l_80e_visdrone.yml | 0 ...loe_crn_l_alpha_largesize_80e_visdrone.yml | 0 .../ppyoloe_crn_l_p2_alpha_80e_visdrone.yml | 0 ..._crn_l_p2_alpha_largesize_80e_visdrone.yml | 0 .../visdrone/ppyoloe_crn_s_80e_visdrone.yml | 0 .../ppyoloe_crn_s_p2_alpha_80e_visdrone.yml | 0 ...oloe_plus_crn_l_largesize_80e_visdrone.yml | 0 .../ppyoloe_plus_sod_crn_l_80e_visdrone.yml} | 0 ...plus_sod_crn_l_largesize_80e_visdrone.yml} | 0 .../ppyoloe_plus_sod_crn_s_80e_visdrone.yml} | 0 configs/visdrone/README.md | 56 ------- demo/0000315_01601_d_0000509.jpg | Bin 0 -> 195638 bytes 14 files changed, 192 insertions(+), 69 deletions(-) create mode 100644 configs/smalldet/visdrone/README.md rename configs/{ => smalldet}/visdrone/ppyoloe_crn_l_80e_visdrone.yml (100%) rename configs/{ => smalldet}/visdrone/ppyoloe_crn_l_alpha_largesize_80e_visdrone.yml (100%) rename configs/{ => smalldet}/visdrone/ppyoloe_crn_l_p2_alpha_80e_visdrone.yml (100%) rename configs/{ => smalldet}/visdrone/ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.yml (100%) rename configs/{ => smalldet}/visdrone/ppyoloe_crn_s_80e_visdrone.yml (100%) rename configs/{ => smalldet}/visdrone/ppyoloe_crn_s_p2_alpha_80e_visdrone.yml (100%) rename configs/{ => smalldet}/visdrone/ppyoloe_plus_crn_l_largesize_80e_visdrone.yml (100%) rename configs/{visdrone/ppyoloe_plus_new_crn_l_80e_visdrone.yml => smalldet/visdrone/ppyoloe_plus_sod_crn_l_80e_visdrone.yml} (100%) rename configs/{visdrone/ppyoloe_plus_new_crn_l_largesize_80e_visdrone.yml => smalldet/visdrone/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.yml} (100%) rename configs/{visdrone/ppyoloe_plus_new_crn_s_80e_visdrone.yml => smalldet/visdrone/ppyoloe_plus_sod_crn_s_80e_visdrone.yml} (100%) delete mode 100644 configs/visdrone/README.md create mode 100755 demo/0000315_01601_d_0000509.jpg diff --git a/configs/smalldet/README.md b/configs/smalldet/README.md index a5babf569..a5ec2b86c 100644 --- a/configs/smalldet/README.md +++ b/configs/smalldet/README.md @@ -1,10 +1,11 @@ -# PP-YOLOE 小目标检测模型(PP-YOLOE smalldet) +# PP-YOLOE 小目标检测模型(PP-YOLOE Small Object Detection) VisDroneVisDroneDOTAXview ## 内容 - [简介](#简介) - [模型库](#模型库) + - [VisDrone模型](#VisDrone模型) - [基础模型](#基础模型) - [拼图模型](#拼图模型) - [数据集准备](#数据集准备) @@ -23,12 +24,36 @@ PaddleDetection团队提供了针对VisDrone-DET、DOTA水平框、Xview等小 **注意:** - **是否需要切图**，建议参照[切图使用说明](#切图使用说明)中的[统计数据集分布](#统计数据集分布)先分析一下数据集再确定，一般数据集所有目标均极小的时候推荐切图训练和切图预测。 -- 不通过切图拼图而直接使用原图的方案也可以参照[visdrone](../visdrone)。 +- 不通过切图拼图而直接使用原图的方案也可以参照[visdrone](./visdrone)。 - 第三方AI Studio教程案例可参考[PPYOLOE：遥感场景下的小目标检测与部署（切图版）](https://aistudio.baidu.com/aistudio/projectdetail/4493701)和[涨分神器！基于PPYOLOE的切图和拼图解决方案](https://aistudio.baidu.com/aistudio/projectdetail/4438275)。 ## 模型库 +### [VisDrone模型](visdrone/) + +| 模型 | COCOAPI mAPval
0.5:0.95 | COCOAPI mAPval
0.5 | COCOAPI mAPtest_dev
0.5:0.95 | COCOAPI mAPtest_dev
0.5 | MatlabAPI mAPtest_dev
0.5:0.95 | MatlabAPI mAPtest_dev
0.5 | 下载 | 配置文件 | +|:---------|:------:|:------:| :----: | :------:| :------: | :------:| :----: | :------:| +|PP-YOLOE-s| 23.5 | 39.9 | 19.4 | 33.6 | 23.68 | 40.66 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_80e_visdrone.pdparams) | [配置文件](visdrone/ppyoloe_crn_s_80e_visdrone.yml) | +|PP-YOLOE-P2-Alpha-s| 24.4 | 41.6 | 20.1 | 34.7 | 24.55 | 42.19 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_p2_alpha_80e_visdrone.pdparams) | [配置文件](visdrone/ppyoloe_crn_s_p2_alpha_80e_visdrone.yml) | +|PP_YOLOE_plus_sod_s| 25.1 | 42.8 | 20.7 | 36.2 | 25.16 | 43.86 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_s_80e_visdrone.pdparams) | [配置文件](visdrone/ppyoloe_plus_sod_crn_s_80e_visdrone.yml) | +|PP-YOLOE-l| 29.2 | 47.3 | 23.5 | 39.1 | 28.00 | 46.20 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_visdrone.pdparams) | [配置文件](visdrone/ppyoloe_crn_l_80e_visdrone.yml) | +|PP-YOLOE-P2-Alpha-l| 30.1 | 48.9 | 24.3 | 40.8 | 28.47 | 48.16 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_p2_alpha_80e_visdrone.pdparams) | [配置文件](visdrone/ppyoloe_crn_l_p2_alpha_80e_visdrone.yml) | +|PP_YOLOE_plus_sod_l| 31.9 | 52.1 | 25.6 | 43.5 | 30.25 | 51.18 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_80e_visdrone.pdparams) | [配置文件](visdrone/ppyoloe_plus_sod_crn_l_80e_visdrone.yml) | +|PP-YOLOE-Alpha-largesize-l| 41.9 | 65.0 | 32.3 | 53.0 | 37.13 | 61.15 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_alpha_largesize_80e_visdrone.pdparams) | [配置文件](visdrone/ppyoloe_crn_l_alpha_largesize_80e_visdrone.yml) | +|PP-YOLOE-P2-Alpha-largesize-l| 41.3 | 64.5 | 32.4 | 53.1 | 37.49 | 51.54 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.pdparams) | [配置文件](visdrone/ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.yml) | +|PP-YOLOE-plus-largesize-l | 43.3 | 66.7 | 33.5 | 54.7 | 38.24 | 62.76 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_largesize_80e_visdrone.pdparams) | [配置文件](visdrone/ppyoloe_plus_crn_l_largesize_80e_visdrone.yml) | +|PP-YOLOE-plus_sod-largesize_l | 42.7 | 65.9 | 33.6 | 55.1 | 38.4 | 63.07 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.pdparams) | [配置文件](visdrone/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.yml) | + +**注意:** + - VisDrone-DET数据集**可使用原图训练，也可使用切图后训练**，上表中的模型均为**使用原图训练**，也使用**原图评估预测**，推荐直接使用带**sod**的模型配置文件去训练评估和预测部署。 + - 上表中的模型指标均是使用VisDrone-DET的train子集作为训练集，使用VisDrone-DET的val子集和test_dev子集作为验证集。 + - **sod**表示使用**基于向量的DFL算法**和针对小目标的**中心先验优化策略**，并**在模型的Neck结构中加入transformer**。 + - **P2**表示增加P2层(1/4下采样层)的特征，共输出4个PPYOLOEHead。 + - **Alpha**表示对CSPResNet骨干网络增加可一个学习权重参数Alpha参与训练。 + - **largesize**表示使用**以1600尺度为基础的多尺度训练**和**1920尺度预测**，相应的训练batch_size也减小，以速度来换取高精度。 + + ### 基础模型 | 模型 | 数据集 | SLICE_SIZE | OVERLAP_RATIO | 类别数 | mAPval
0.5:0.95 | APval
0.5 | 下载链接 | 配置文件 | @@ -37,21 +62,32 @@ PaddleDetection团队提供了针对VisDrone-DET、DOTA水平框、Xview等小 |PP-YOLOE-P2-l| Xview | 400 | 0.25 | 60 | 14.9 | 27.0 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_p2_crn_l_80e_sliced_xview_400_025.pdparams) | [配置文件](./ppyoloe_p2_crn_l_80e_sliced_xview_400_025.yml) | |PP-YOLOE-l| VisDrone-DET| 640 | 0.25 | 10 | 38.5 | 60.2 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](./ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml) | +**注意:** + - 上表中的模型均为使用**切图后的子图训练**，使用**子图评估预测**。 + + ### 拼图模型 | 模型 | 数据集 | SLICE_SIZE | OVERLAP_RATIO | 类别数 | mAPval
0.5:0.95 | APval
0.5 | 下载链接 | 配置文件 | |:---------|:---------------:|:---------------:|:---------------:|:------:|:-----------------------:|:-------------------:|:---------:| :-----: | -|PP-YOLOE-l| VisDrone-DET| 640 | 0.25 | 10 | 29.7 | 48.5 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](./ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml) | -|PP-YOLOE-l (Assembled)| VisDrone-DET| 640 | 0.25 | 10 | 37.2 | 59.4 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](./ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml) | +|PP-YOLOE-l (原图评估)| VisDrone-DET| 640 | 0.25 | 10 | 29.7 | 48.5 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](./ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml) | +|PP-YOLOE-l (拼图评估)| VisDrone-DET| 640 | 0.25 | 10 | 37.2 | 59.4 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](./ppyoloe_crn_l_80e_sliced_visdrone_640_025_slice_infer.yml) | **注意:** -- 使用[SAHI](https://github.com/obss/sahi)切图工具需要首先安装：`pip install sahi`，参考[installation](https://github.com/obss/sahi/blob/main/README.md#installation)。 -- **SLICE_SIZE**表示使用SAHI工具切图后子图的边长大小，**OVERLAP_RATIO**表示切图的子图之间的重叠率，DOTA水平框和Xview数据集均是切图后训练，AP指标为切图后的子图val上的指标。 -- VisDrone-DET数据集请参照[visdrone](../visdrone)，可使用原图训练，也可使用切图后训练，这上面表格中的指标均是使用VisDrone-DET的val子集做验证而未使用test_dev子集。 + - 上表中的模型均为使用**切图后的子图**训练，评估预测时分为两种，使用原图评估预测，和使用子图拼图评估预测。 + - **SLICE_SIZE**表示使用SAHI工具切图后子图的边长大小，**OVERLAP_RATIO**表示切图的子图之间的重叠率。 + + +### 注意事项 + +- 切图和拼图，需要使用[SAHI](https://github.com/obss/sahi)切图工具，需要首先安装：`pip install sahi`，参考[installation](https://github.com/obss/sahi/blob/main/README.md#installation)。 +- DOTA水平框和Xview数据集均是**切图后训练**，AP指标为**切图后的子图val上的指标**。 +- VisDrone-DET数据集请参照[visdrone](./visdrone)，**可使用原图训练，也可使用切图后训练**，这上面表格中的指标均是使用VisDrone-DET的val子集做验证而未使用test_dev子集。 - PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练，如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lrnew = lrdefault * (batch_sizenew * GPU_numbernew) / (batch_sizedefault * GPU_numberdefault)** 调整学习率。 -- 常用训练验证部署等步骤请参考[ppyoloe](../ppyoloe#getting-start)。 +- 常用训练验证部署等步骤请参考[ppyoloe](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/ppyoloe#getting-start)。 - 自动切图和拼图的推理预测需添加设置`--slice_infer`，具体见下文[模型库使用说明](#模型库使用说明)中的[预测](#预测)和[部署](#部署)。 -- Assembled表示自动切图和拼图，参照[2.3 子图拼图评估](#评估)。 +- 自动切图和拼图过程，参照[2.3 子图拼图评估](#评估)。 + ## 数据集准备 @@ -232,14 +268,14 @@ EvalDataset: #### 3.1 子图或原图直接预测 与评估流程基本相同，可以在提前切好并存下来的子图上预测，也可以对原图预测，如： ```bash -CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams --infer_img=demo.jpg --draw_threshold=0.25 +CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams --infer_img=demo/0000315_01601_d_0000509.jpg --draw_threshold=0.25 ``` #### 3.2 原图自动切图并拼图预测 也可以对原图进行自动切图并拼图重组来预测原图，如： ```bash # 单张图 -CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams --infer_img=demo.jpg --draw_threshold=0.25 --slice_infer --slice_size 640 640 --overlap_ratio 0.25 0.25 --combine_method=nms --match_threshold=0.6 --match_metric=ios +CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams --infer_img=demo/0000315_01601_d_0000509.jpg --draw_threshold=0.25 --slice_infer --slice_size 640 640 --overlap_ratio 0.25 0.25 --combine_method=nms --match_threshold=0.6 --match_metric=ios # 或图片文件夹 CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams --infer_dir=demo/ --draw_threshold=0.25 --slice_infer --slice_size 640 640 --overlap_ratio 0.25 0.25 --combine_method=nms --match_threshold=0.6 --match_metric=ios ``` @@ -261,14 +297,14 @@ CUDA_VISIBLE_DEVICES=0 python tools/export_model.py -c configs/smalldet/ppyoloe_ #### 4.2 使用原图或子图直接推理 ```bash # deploy infer -CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_80e_sliced_visdrone_640_025 --image_file=demo.jpg --device=GPU --save_images --threshold=0.25 +CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_80e_sliced_visdrone_640_025 --image_file=demo/0000315_01601_d_0000509.jpg --device=GPU --save_images --threshold=0.25 ``` #### 4.3 使用原图自动切图并拼图重组结果来推理 ```bash # deploy slice infer # 单张图 -CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_80e_sliced_visdrone_640_025 --image_file=demo.jpg --device=GPU --save_images --threshold=0.25 --slice_infer --slice_size 640 640 --overlap_ratio 0.25 0.25 --combine_method=nms --match_threshold=0.6 --match_metric=ios +CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_80e_sliced_visdrone_640_025 --image_file=demo/0000315_01601_d_0000509.jpg --device=GPU --save_images --threshold=0.25 --slice_infer --slice_size 640 640 --overlap_ratio 0.25 0.25 --combine_method=nms --match_threshold=0.6 --match_metric=ios # 或图片文件夹 CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_crn_l_80e_sliced_visdrone_640_025 --image_dir=demo/ --device=GPU --save_images --threshold=0.25 --slice_infer --slice_size 640 640 --overlap_ratio 0.25 0.25 --combine_method=nms --match_threshold=0.6 --match_metric=ios ``` diff --git a/configs/smalldet/visdrone/README.md b/configs/smalldet/visdrone/README.md new file mode 100644 index 000000000..08328872d --- /dev/null +++ b/configs/smalldet/visdrone/README.md @@ -0,0 +1,143 @@ +# VisDrone-DET 小目标检测模型 + +PaddleDetection团队提供了针对VisDrone-DET小目标数航拍场景的基于PP-YOLOE的检测模型，用户可以下载模型进行使用。整理后的COCO格式VisDrone-DET数据集[下载链接](https://bj.bcebos.com/v1/paddledet/data/smalldet/visdrone.zip)，检测其中的10类，包括 `pedestrian(1), people(2), bicycle(3), car(4), van(5), truck(6), tricycle(7), awning-tricycle(8), bus(9), motor(10)`，原始数据集[下载链接](https://github.com/VisDrone/VisDrone-Dataset)。 + +**注意:** +- VisDrone-DET数据集包括**train集6471张，val集548张，test_dev集1610张**，test-challenge集1580张(未开放检测框标注)，前三者均有开放检测框标注。 +- 模型均**只使用train集训练**，在val集和test_dev集上分别验证精度，test_dev集图片数较多，精度参考性较高。 + + +## 原图训练，原图评估： + +| 模型 | COCOAPI mAPval
0.5:0.95 | COCOAPI mAPval
0.5 | COCOAPI mAPtest_dev
0.5:0.95 | COCOAPI mAPtest_dev
0.5 | MatlabAPI mAPtest_dev
0.5:0.95 | MatlabAPI mAPtest_dev
0.5 | 下载 | 配置文件 | +|:---------|:------:|:------:| :----: | :------:| :------: | :------:| :----: | :------:| +|PP-YOLOE-s| 23.5 | 39.9 | 19.4 | 33.6 | 23.68 | 40.66 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_s_80e_visdrone.yml) | +|PP-YOLOE-P2-Alpha-s| 24.4 | 41.6 | 20.1 | 34.7 | 24.55 | 42.19 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_p2_alpha_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_s_p2_alpha_80e_visdrone.yml) | +|PP_YOLOE_plus_sod_s| 25.1 | 42.8 | 20.7 | 36.2 | 25.16 | 43.86 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_s_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_sod_crn_s_80e_visdrone.yml) | +|PP-YOLOE-l| 29.2 | 47.3 | 23.5 | 39.1 | 28.00 | 46.20 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_80e_visdrone.yml) | +|PP-YOLOE-P2-Alpha-l| 30.1 | 48.9 | 24.3 | 40.8 | 28.47 | 48.16 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_p2_alpha_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_p2_alpha_80e_visdrone.yml) | +|PP_YOLOE_plus_sod_l| 31.9 | 52.1 | 25.6 | 43.5 | 30.25 | 51.18 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_sod_crn_l_80e_visdrone.yml) | +|PP-YOLOE-Alpha-largesize-l| 41.9 | 65.0 | 32.3 | 53.0 | 37.13 | 61.15 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_alpha_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_alpha_largesize_80e_visdrone.yml) | +|PP-YOLOE-P2-Alpha-largesize-l| 41.3 | 64.5 | 32.4 | 53.1 | 37.49 | 51.54 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.yml) | +|PP-YOLOE-plus-largesize-l | 43.3 | 66.7 | 33.5 | 54.7 | 38.24 | 62.76 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_crn_l_largesize_80e_visdrone.yml) | +|PP-YOLOE-plus_sod-largesize_l | 42.7 | 65.9 | 33.6 | 55.1 | 38.4 | 63.07 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.yml) | + +**注意:** + - 上表中的模型均为**使用原图训练**，也使用**原图评估预测**。 + - **sod**表示使用**基于向量的DFL算法**和针对小目标的**中心先验优化策略**，并**在模型的Neck结构中加入transformer**。 + - **P2**表示增加P2层(1/4下采样层)的特征，共输出4个PPYOLOEHead。 + - **Alpha**表示对CSPResNet骨干网络增加可一个学习权重参数Alpha参与训练。 + - **largesize**表示使用**以1600尺度为基础的多尺度训练**和**1920尺度预测**，相应的训练batch_size也减小，以速度来换取高精度。 + + +## 子图训练，原图评估和拼图评估： + +| 模型 | 数据集 | SLICE_SIZE | OVERLAP_RATIO | 类别数 | mAPval
0.5:0.95 | APval
0.5 | 下载链接 | 配置文件 | +|:---------|:---------------:|:---------------:|:---------------:|:------:|:-----------------------:|:-------------------:|:---------:| :-----: | +|PP-YOLOE-l(原图评估)| VisDrone-DET| 640 | 0.25 | 10 | 29.7 | 48.5 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](../ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml) | +|PP-YOLOE-l (拼图评估)| VisDrone-DET| 640 | 0.25 | 10 | 37.2 | 59.4 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](../ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml) | + +**注意:** + - 上表中的模型均为使用**切图后的子图**训练，评估预测时分为两种，使用原图评估预测，和使用子图拼图评估预测。 + - **SLICE_SIZE**表示使用SAHI工具切图后子图的边长大小，**OVERLAP_RATIO**表示切图的子图之间的重叠率。 + + +## 注意事项： + - PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练，如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lrnew = lrdefault * (batch_sizenew * GPU_numbernew) / (batch_sizedefault * GPU_numberdefault)** 调整学习率。 + - 具体使用教程请参考[ppyoloe](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/ppyoloe#getting-start)。 + - MatlabAPI测试是使用官网评测工具[VisDrone2018-DET-toolkit](https://github.com/VisDrone/VisDrone2018-DET-toolkit)。 + - 切图训练模型的配置文件及训练相关流程请参照[README](../README.cn)。 + + +## 部署模型 + +| 网络模型 | 输入尺寸 | 导出后的权重(w/o NMS) | ONNX(w/o NMS) | +| :-------- | :--------: | :---------------------: | :----------------: | +| PP_YOLOE_plus_sod_s | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_s_80e_visdrone_w_nms.zip) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_s_80e_visdrone_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_s_80e_visdrone_w_nms.onnx) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_s_80e_visdrone_wo_nms.onnx) | +| PP_YOLOE_plus_sod_l | 640 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_80e_visdrone_w_nms.zip) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_80e_visdrone_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_80e_visdrone_w_nms.onnx) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_80e_visdrone_wo_nms.onnx) | +| PP-YOLOE-plus_sod-largesize_l | 1920 | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone_w_nms.zip) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone_wo_nms.zip) | [( w/ nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone_w_nms.onnx) | [( w/o nms)](https://paddledet.bj.bcebos.com/deploy/smalldet/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone_wo_nms.onnx) | + + +## 测速 + +1.参考[Paddle Inference文档](https://www.paddlepaddle.org.cn/inference/master/user_guides/download_lib.html#python)，下载并安装与你的CUDA, CUDNN和TensorRT相应的wheel包。 +测速需要设置`--run_benchmark=True`, 你需要安装以下依赖`pip install pynvml psutil GPUtil`。 +导出ONNX，你需要安装以下依赖`pip install paddle2onnx`。 + +2.运行以下命令导出**带NMS的模型和ONNX**，并使用TensorRT FP16进行推理和测速 + +### 注意： + +- 由于NMS参数设置对速度影响极大，部署测速时可调整`keep_top_k`和`nms_top_k`，在只低约0.1 mAP精度的情况下加快预测速度，导出模型的时候也可这样设置： + ``` + nms: + name: MultiClassNMS + nms_top_k: 1000 # 10000 + keep_top_k: 100 # 500 + score_threshold: 0.01 + nms_threshold: 0.6 + ``` + +```bash +# 导出带NMS的模型 +python tools/export_model.py -c configs/smalldet/visdrone/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.pdparams trt=True + +# 导出带NMS的ONNX +paddle2onnx --model_dir output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.onnx + +# 推理单张图片 +CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/0000315_01601_d_0000509.jpg --device=gpu --run_mode=trt_fp16 + +# 推理文件夹下的所有图片 +CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_dir=demo/ --device=gpu --run_mode=trt_fp16 + +# 单张图片普通测速 +CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/0000315_01601_d_0000509.jpg --device=gpu --run_benchmark=True + +# 单张图片TensorRT FP16测速 +CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/0000315_01601_d_0000509.jpg --device=gpu --run_benchmark=True --run_mode=trt_fp16 +``` + +3.运行以下命令导出**不带NMS的模型和ONNX**，并使用TensorRT FP16进行推理和测速，以及**ONNX下FP16测速** + +```bash +# 导出带NMS的模型 +python tools/export_model.py -c configs/smalldet/visdrone/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.yml -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.pdparams trt=True exclude_nms=True + +# 导出带NMS的ONNX +paddle2onnx --model_dir output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --model_filename model.pdmodel --params_filename model.pdiparams --opset_version 12 --save_file ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.onnx + +# 推理单张图片 +CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/0000315_01601_d_0000509.jpg --device=gpu --run_mode=trt_fp16 + +# 推理文件夹下的所有图片 +CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_dir=demo/ --device=gpu --run_mode=trt_fp16 + +# 单张图片普通测速 +CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/0000315_01601_d_0000509.jpg --device=gpu --run_benchmark=True + +# 单张图片TensorRT FP16测速 +CUDA_VISIBLE_DEVICES=0 python deploy/python/infer.py --model_dir=output_inference/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone --image_file=demo/0000315_01601_d_0000509.jpg --device=gpu --run_benchmark=True --run_mode=trt_fp16 + +# 单张图片ONNX TensorRT FP16测速 +/usr/local/TensorRT-8.0.3.4/bin/trtexec --onnx=ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.onnx --workspace=4096 --avgRuns=10 --shapes=input:1x3x1920x1920 --fp16 +``` + +**注意：** +- TensorRT会根据网络的定义，执行针对当前硬件平台的优化，生成推理引擎并序列化为文件。该推理引擎只适用于当前软硬件平台。如果你的软硬件平台没有发生变化，你可以设置[enable_tensorrt_engine](https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/deploy/python/infer.py#L857)的参数`use_static=True`，这样生成的序列化文件将会保存在`output_inference`文件夹下，下次执行TensorRT时将加载保存的序列化文件。 +- PaddleDetection release/2.4及其之后的版本将支持NMS调用TensorRT，需要依赖PaddlePaddle release/2.3及其之后的版本 + + +# 引用 +``` +@ARTICLE{9573394, + author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin}, + journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, + title={Detection and Tracking Meet Drones Challenge}, + year={2021}, + volume={}, + number={}, + pages={1-1}, + doi={10.1109/TPAMI.2021.3119563} +} +``` diff --git a/configs/visdrone/ppyoloe_crn_l_80e_visdrone.yml b/configs/smalldet/visdrone/ppyoloe_crn_l_80e_visdrone.yml similarity index 100% rename from configs/visdrone/ppyoloe_crn_l_80e_visdrone.yml rename to configs/smalldet/visdrone/ppyoloe_crn_l_80e_visdrone.yml diff --git a/configs/visdrone/ppyoloe_crn_l_alpha_largesize_80e_visdrone.yml b/configs/smalldet/visdrone/ppyoloe_crn_l_alpha_largesize_80e_visdrone.yml similarity index 100% rename from configs/visdrone/ppyoloe_crn_l_alpha_largesize_80e_visdrone.yml rename to configs/smalldet/visdrone/ppyoloe_crn_l_alpha_largesize_80e_visdrone.yml diff --git a/configs/visdrone/ppyoloe_crn_l_p2_alpha_80e_visdrone.yml b/configs/smalldet/visdrone/ppyoloe_crn_l_p2_alpha_80e_visdrone.yml similarity index 100% rename from configs/visdrone/ppyoloe_crn_l_p2_alpha_80e_visdrone.yml rename to configs/smalldet/visdrone/ppyoloe_crn_l_p2_alpha_80e_visdrone.yml diff --git a/configs/visdrone/ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.yml b/configs/smalldet/visdrone/ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.yml similarity index 100% rename from configs/visdrone/ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.yml rename to configs/smalldet/visdrone/ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.yml diff --git a/configs/visdrone/ppyoloe_crn_s_80e_visdrone.yml b/configs/smalldet/visdrone/ppyoloe_crn_s_80e_visdrone.yml similarity index 100% rename from configs/visdrone/ppyoloe_crn_s_80e_visdrone.yml rename to configs/smalldet/visdrone/ppyoloe_crn_s_80e_visdrone.yml diff --git a/configs/visdrone/ppyoloe_crn_s_p2_alpha_80e_visdrone.yml b/configs/smalldet/visdrone/ppyoloe_crn_s_p2_alpha_80e_visdrone.yml similarity index 100% rename from configs/visdrone/ppyoloe_crn_s_p2_alpha_80e_visdrone.yml rename to configs/smalldet/visdrone/ppyoloe_crn_s_p2_alpha_80e_visdrone.yml diff --git a/configs/visdrone/ppyoloe_plus_crn_l_largesize_80e_visdrone.yml b/configs/smalldet/visdrone/ppyoloe_plus_crn_l_largesize_80e_visdrone.yml similarity index 100% rename from configs/visdrone/ppyoloe_plus_crn_l_largesize_80e_visdrone.yml rename to configs/smalldet/visdrone/ppyoloe_plus_crn_l_largesize_80e_visdrone.yml diff --git a/configs/visdrone/ppyoloe_plus_new_crn_l_80e_visdrone.yml b/configs/smalldet/visdrone/ppyoloe_plus_sod_crn_l_80e_visdrone.yml similarity index 100% rename from configs/visdrone/ppyoloe_plus_new_crn_l_80e_visdrone.yml rename to configs/smalldet/visdrone/ppyoloe_plus_sod_crn_l_80e_visdrone.yml diff --git a/configs/visdrone/ppyoloe_plus_new_crn_l_largesize_80e_visdrone.yml b/configs/smalldet/visdrone/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.yml similarity index 100% rename from configs/visdrone/ppyoloe_plus_new_crn_l_largesize_80e_visdrone.yml rename to configs/smalldet/visdrone/ppyoloe_plus_sod_crn_l_largesize_80e_visdrone.yml diff --git a/configs/visdrone/ppyoloe_plus_new_crn_s_80e_visdrone.yml b/configs/smalldet/visdrone/ppyoloe_plus_sod_crn_s_80e_visdrone.yml similarity index 100% rename from configs/visdrone/ppyoloe_plus_new_crn_s_80e_visdrone.yml rename to configs/smalldet/visdrone/ppyoloe_plus_sod_crn_s_80e_visdrone.yml diff --git a/configs/visdrone/README.md b/configs/visdrone/README.md deleted file mode 100644 index da97971d7..000000000 --- a/configs/visdrone/README.md +++ /dev/null @@ -1,56 +0,0 @@ -# VisDrone-DET 检测模型 - -PaddleDetection团队提供了针对VisDrone-DET小目标数航拍场景的基于PP-YOLOE的检测模型，用户可以下载模型进行使用。整理后的COCO格式VisDrone-DET数据集[下载链接](https://bj.bcebos.com/v1/paddledet/data/smalldet/visdrone.zip)，检测其中的10类，包括 `pedestrian(1), people(2), bicycle(3), car(4), van(5), truck(6), tricycle(7), awning-tricycle(8), bus(9), motor(10)`，原始数据集[下载链接](https://github.com/VisDrone/VisDrone-Dataset)。 - -**注意:** -- VisDrone-DET数据集包括train集6471张，val集548张，test_dev集1610张，test-challenge集1580张(未开放检测框标注)，前三者均有开放检测框标注。 -- 模型均只使用train集训练，在val集和test_dev集上验证精度，test_dev集图片数较多，精度参考性较高。 - - -## 原图训练： - -| 模型 | COCOAPI mAPval
0.5:0.95 | COCOAPI mAPval
0.5 | COCOAPI mAPtest_dev
0.5:0.95 | COCOAPI mAPtest_dev
0.5 | MatlabAPI mAPtest_dev
0.5:0.95 | MatlabAPI mAPtest_dev
0.5 | 下载 | 配置文件 | -|:---------|:------:|:------:| :----: | :------:| :------: | :------:| :----: | :------:| -|PP-YOLOE-s| 23.5 | 39.9 | 19.4 | 33.6 | 23.68 | 40.66 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_s_80e_visdrone.yml) | -|PP-YOLOE-P2-Alpha-s| 24.4 | 41.6 | 20.1 | 34.7 | 24.55 | 42.19 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_s_p2_alpha_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_s_p2_alpha_80e_visdrone.yml) | -|PP_YOLOE_plus_new_s| 25.1 | 42.8 | 20.7 | 36.2 | 25.16 | 43.86 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_new_crn_s_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_new_crn_s_80e_visdrone.yml) | -|PP-YOLOE-l| 29.2 | 47.3 | 23.5 | 39.1 | 28.00 | 46.20 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_80e_visdrone.yml) | -|PP-YOLOE-P2-Alpha-l| 30.1 | 48.9 | 24.3 | 40.8 | 28.47 | 48.16 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_p2_alpha_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_p2_alpha_80e_visdrone.yml) | -|PP_YOLOE_plus_new_l| 31.9 | 52.1 | 25.6 | 43.5 | 30.25 | 51.18 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_new_crn_l_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_new_crn_l_80e_visdrone.yml) | -|PP-YOLOE-Alpha-largesize-l| 41.9 | 65.0 | 32.3 | 53.0 | 37.13 | 61.15 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_alpha_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_alpha_largesize_80e_visdrone.yml) | -|PP-YOLOE-P2-Alpha-largesize-l| 41.3 | 64.5 | 32.4 | 53.1 | 37.49 | 51.54 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_crn_l_p2_alpha_largesize_80e_visdrone.yml) | -|PP-YOLOE-plus-largesize-l | 43.3 | 66.7 | 33.5 | 54.7 | 38.24 | 62.76 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_l_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_crn_l_largesize_80e_visdrone.yml) | -|PP-YOLOE-plus_new-largesize_l | 42.7 | 65.9 | 33.6 | 55.1 | 38.4 | 63.07 | [下载链接](https://paddledet.bj.bcebos.com/models/ppyoloe_plus_new_crn_l_largesize_80e_visdrone.pdparams) | [配置文件](./ppyoloe_plus_new_crn_l_largesize_80e_visdrone.yml) | - - -## 原图评估和拼图评估对比： - -| 模型 | 数据集 | SLICE_SIZE | OVERLAP_RATIO | 类别数 | mAPval
0.5:0.95 | APval
0.5 | 下载链接 | 配置文件 | -|:---------|:---------------:|:---------------:|:---------------:|:------:|:-----------------------:|:-------------------:|:---------:| :-----: | -|PP-YOLOE-l| VisDrone-DET| 640 | 0.25 | 10 | 29.7 | 48.5 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](../smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml) | -|PP-YOLOE-l (Assembled)| VisDrone-DET| 640 | 0.25 | 10 | 37.2 | 59.4 | [下载链接](https://bj.bcebos.com/v1/paddledet/models/ppyoloe_crn_l_80e_sliced_visdrone_640_025.pdparams) | [配置文件](../smalldet/ppyoloe_crn_l_80e_sliced_visdrone_640_025.yml) | - - -**注意:** -- PP-YOLOE模型训练过程中使用8 GPUs进行混合精度训练，如果**GPU卡数**或者**batch size**发生了改变，你需要按照公式 **lrnew = lrdefault * (batch_sizenew * GPU_numbernew) / (batch_sizedefault * GPU_numberdefault)** 调整学习率。 -- 具体使用教程请参考[ppyoloe](../ppyoloe#getting-start)。 -- P2表示增加P2层(1/4下采样层)的特征，共输出4个PPYOLOEHead。 -- Alpha表示对CSPResNet骨干网络增加可一个学习权重参数Alpha参与训练。 -- largesize表示使用以1600尺度为基础的多尺度训练和1920尺度预测，相应的训练batch_size也减小，以速度来换取高精度。 -- MatlabAPI测试是使用官网评测工具[VisDrone2018-DET-toolkit](https://github.com/VisDrone/VisDrone2018-DET-toolkit)。 -- 切图训练模型的配置文件及训练相关流程请参照[smalldet](../smalldet)。 - - -## 引用 -``` -@ARTICLE{9573394, - author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin}, - journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, - title={Detection and Tracking Meet Drones Challenge}, - year={2021}, - volume={}, - number={}, - pages={1-1}, - doi={10.1109/TPAMI.2021.3119563} -} -``` diff --git a/demo/0000315_01601_d_0000509.jpg b/demo/0000315_01601_d_0000509.jpg new file mode 100755 index 0000000000000000000000000000000000000000..cc7a3602c1c015213ca1f7e27b0d006e827ee935 GIT binary patch literal 195638 zcmbTdWl$Vn_%%2%I0S+Qx4|uFU~mcU?g{QTxcu;7lVM+|zUN*q<3P28kg@J*I zfsTcViHVJkg@Z@(3J(_-kAjGpfP{vUj+Ta!nwp-8hn1d@3q(!LCd|&o%O@x(NXH76 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