From 194119635b96e4aaa0eb7398ec2593931c41d27b Mon Sep 17 00:00:00 2001 From: YixinKristy <48054808+YixinKristy@users.noreply.github.com> Date: Mon, 28 Mar 2022 18:24:17 +0800 Subject: [PATCH] Update attribute_en.md --- deploy/pphuman/docs/attribute_en.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deploy/pphuman/docs/attribute_en.md b/deploy/pphuman/docs/attribute_en.md index 1b41678f7..38cbc7a78 100644 --- a/deploy/pphuman/docs/attribute_en.md +++ b/deploy/pphuman/docs/attribute_en.md @@ -9,8 +9,8 @@ Pedestrian attribute recognition has been widely used in the intelligent communi | Pedestrian Detection/ Tracking | PP-YOLOE | mAP: 56.3
MOTA: 72.0 | Detection: 28ms
Trackingļ¼š33.1ms | [Download Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_pipeline.zip) | | Pedestrian Attribute Analysis | StrongBaseline | ma: 94.86 | Per Person 2ms | [Download Link](https://bj.bcebos.com/v1/paddledet/models/pipeline/strongbaseline_r50_30e_pa100k.tar) | -1. The precision of detection and tracking models is obtained by training and testing on the integration of MOT17, CrowdHuman, HIEVE, and some business data. -2. The precision of pedestiran attribute analysis is obtained by training and testing on the integration of PA100k, RAPv2, PETA, and some business data. +1. The precision of detection/ tracking models is obtained by training and testing on the dataset consist of MOT17, CrowdHuman, HIEVE, and some business data. +2. The precision of pedestiran attribute analysis is obtained by training and testing on the dataset consist of PA100k, RAPv2, PETA, and some business data. 3. The inference speed is T4, the speed of using TensorRT FP16. ## Instruction -- GitLab