From 2382374e7d2161aa95a33c28be4ad1c75b17ae58 Mon Sep 17 00:00:00 2001 From: JYChen Date: Mon, 29 Aug 2022 15:36:48 +0800 Subject: [PATCH] fix bug in pipeline doc and update kpt training config (#6769) (#6783) --- .../{hrnet_w32_256x192.yml => dark_hrnet_w32_256x192.yml} | 4 ++-- deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md | 2 +- deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md | 2 +- deploy/pipeline/docs/tutorials/ppvehicle_attribute.md | 6 +++--- deploy/pipeline/docs/tutorials/ppvehicle_attribute_en.md | 6 +++--- 5 files changed, 10 insertions(+), 10 deletions(-) rename configs/pphuman/{hrnet_w32_256x192.yml => dark_hrnet_w32_256x192.yml} (96%) diff --git a/configs/pphuman/hrnet_w32_256x192.yml b/configs/pphuman/dark_hrnet_w32_256x192.yml similarity index 96% rename from configs/pphuman/hrnet_w32_256x192.yml rename to configs/pphuman/dark_hrnet_w32_256x192.yml index 37782b748..8418245ed 100644 --- a/configs/pphuman/hrnet_w32_256x192.yml +++ b/configs/pphuman/dark_hrnet_w32_256x192.yml @@ -101,7 +101,7 @@ TrainReader: flip_pairs: *flip_perm - TopDownAffine: trainsize: *trainsize - - ToHeatmapsTopDown: + - ToHeatmapsTopDown_DARK: hmsize: *hmsize sigma: 2 batch_transforms: @@ -125,6 +125,7 @@ EvalReader: is_scale: true - Permute: {} batch_size: 16 + drop_empty: false TestReader: inputs_def: @@ -139,4 +140,3 @@ TestReader: is_scale: true - Permute: {} batch_size: 1 - fuse_normalize: false #whether to fuse nomalize layer into model while export model diff --git a/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md b/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md index e4861ed8c..2eaf7dfad 100644 --- a/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md +++ b/deploy/pipeline/docs/tutorials/PPHuman_QUICK_STARTED.md @@ -55,7 +55,7 @@ PP-Human提供了目标检测、属性识别、行为识别、ReID预训练模 | 闯入识别 | 31.8ms | [多目标跟踪](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 多目标跟踪：182M | | 打架识别 | 19.7ms | [视频分类](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | 90M | | 抽烟识别 | 单人15.1ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[基于人体id的目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/ppyoloe_crn_s_80e_smoking_visdrone.zip) | 目标检测：182M
基于人体id的目标检测：27M | -| 打电话识别 | 单人ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[基于人体id的图像分类](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | 目标检测：182M
基于人体id的图像分类：45M | +| 打电话识别 | 单人6.0ms | [目标检测](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip)
[基于人体id的图像分类](https://bj.bcebos.com/v1/paddledet/models/pipeline/PPHGNet_tiny_calling_halfbody.zip) | 目标检测：182M
基于人体id的图像分类：45M | 下载模型后，解压至`./output_inference`文件夹。 diff --git a/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md b/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md index 6736ba7c6..10be312d3 100644 --- a/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md +++ b/deploy/pipeline/docs/tutorials/PPVehicle_QUICK_STARTED.md @@ -194,7 +194,7 @@ PP-Vehicle 整体方案如下图所示: ### 车牌识别 - 使用PaddleOCR特色模型ch_PP-OCRv3_det+ch_PP-OCRv3_rec模型，识别车牌号码 -- 详细文档参考[属性识别](ppvehicle_plate.md) +- 详细文档参考[车牌识别](ppvehicle_plate.md) ### 违法停车识别 - 车辆跟踪模型使用高精度模型PP-YOLOE L，根据车辆的跟踪轨迹以及指定的违停区域判断是否违法停车，如果存在则展示违法停车车牌号。 diff --git a/deploy/pipeline/docs/tutorials/ppvehicle_attribute.md b/deploy/pipeline/docs/tutorials/ppvehicle_attribute.md index 46a4d131c..a9abaf4c0 100644 --- a/deploy/pipeline/docs/tutorials/ppvehicle_attribute.md +++ b/deploy/pipeline/docs/tutorials/ppvehicle_attribute.md @@ -6,12 +6,12 @@ | 任务 | 算法 | 精度 | 预测速度 | 下载链接| |-----------|------|-----------|----------|---------------| -| 车辆检测/跟踪 | PP-YOLOE | - | - | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | -| 车辆属性识别 | PPLCNet | 90.81 | 2.36 ms | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | +| 车辆检测/跟踪 | PP-YOLOE | mAP 63.9 | 38.67ms | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | +| 车辆属性识别 | PPLCNet | 90.81 | 7.31 ms | [预测部署模型](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | 注意： -1. 属性模型预测速度是基于Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz 测试得到，开启 MKLDNN 加速策略，线程数为10。 +1. 属性模型预测速度是基于NVIDIA T4, 开启TensorRT FP16得到。模型预测速度包含数据预处理、模型预测、后处理部分。 2. 关于PP-LCNet的介绍可以参考[PP-LCNet](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)介绍，相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099)。 3. 属性模型的训练和精度测试均基于[VeRi数据集](https://www.v7labs.com/open-datasets/veri-dataset)。 diff --git a/deploy/pipeline/docs/tutorials/ppvehicle_attribute_en.md b/deploy/pipeline/docs/tutorials/ppvehicle_attribute_en.md index 6429abf92..5731244bf 100644 --- a/deploy/pipeline/docs/tutorials/ppvehicle_attribute_en.md +++ b/deploy/pipeline/docs/tutorials/ppvehicle_attribute_en.md @@ -6,12 +6,12 @@ Vehicle attribute recognition is widely used in smart cities, smart transportati | Task | Algorithm | Precision | Inference Speed | Download | |-----------|------|-----------|----------|---------------------| -| Vehicle Detection/Tracking | PP-YOLOE | - | - | [Inference and Deployment Model](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | -| Vehicle Attribute Recognition | PPLCNet | 90.81 | 2.36 ms | [Inference and Deployment Model](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | +| Vehicle Detection/Tracking | PP-YOLOE | mAP 63.9 | 38.67ms | [Inference and Deployment Model](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip) | +| Vehicle Attribute Recognition | PPLCNet | 90.81 | 7.31 ms | [Inference and Deployment Model](https://bj.bcebos.com/v1/paddledet/models/pipeline/vehicle_attribute_model.zip) | Note: -1. The inference speed of the attribute model is obtained from the test on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, with the MKLDNN acceleration strategy enabled, and 10 threads. +1. The inference speed of the attribute model is obtained from the test on NVIDIA T4, with TensorRT FP16. The time includes data pre-process, model inference and post-process. 2. For introductions, please refer to [PP-LCNet Series](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/en/models/PP-LCNet_en.md). Related paper is available on PP-LCNet paper 3. The training and test phase of vehicle attribute recognition model are both obtained from [VeRi dataset](https://www.v7labs.com/open-datasets/veri-dataset). -- GitLab