From 7ed90b82404165baf34261310de53d19ea5fc84c Mon Sep 17 00:00:00 2001
From: YixinKristy <48054808+YixinKristy@users.noreply.github.com>
Date: Mon, 28 Mar 2022 19:09:08 +0800
Subject: [PATCH] Add Attribute English Doc (#5480)

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+[English](attribute_en.md) | 简体中文
+
 # PP-Human属性识别模块
 
 行人属性识别在智慧社区，工业巡检，交通监控等方向都具有广泛应用，PP-Human中集成了属性识别模块，属性包含性别、年龄、帽子、眼镜、上衣下衣款式等。我们提供了预训练模型，用户可以直接下载使用。
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+English | [简体中文](attribute.md)
+
+# Attribute Recognition Modules of PP-Human
+
+Pedestrian attribute recognition has been widely used in the intelligent community, industrial, and transportation monitoring. Many attribute recognition modules have been gathered in PP-Human, including gender, age, hats, eyes, clothing and up to 26 attributes in total. Also, the pre-trained models are offered here and users can download and use them directly.
+
+| Task                 | Algorithm | Precision | Inference Speed(ms) | Download Link                                                                               |
+|:---------------------|:---------:|:------:|:------:| :---------------------------------------------------------------------------------: |
+| Pedestrian Detection/ Tracking    |  PP-YOLOE | mAP: 56.3 <br> MOTA: 72.0 | Detection: 28ms <br> Tracking：33.1ms | [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/ 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
+
+1. Download the model from the link in the above table, and unzip it to```./output_inference```.
+2. When inputting the image, run the command as follows:
+```python
+python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \
+                                                   --image_file=test_image.jpg \
+                                                   --device=gpu \
+                                                   --enable_attr=True
+```
+3. When inputting the video, run the command as follows:
+```python
+python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \
+                                                   --video_file=test_video.mp4 \
+                                                   --device=gpu \
+                                                   --enable_attr=True
+```
+4. If you want to change the model path, there are two methods：
+
+    - In ```./deploy/pphuman/config/infer_cfg.yml``` you can configurate different model paths. In attribute recognition models, you can modify the configuration in the field of ATTR.
+    - Add `--model_dir` in the command line to change the model path：
+```python
+python deploy/pphuman/pipeline.py --config deploy/pphuman/config/infer_cfg.yml \
+                                                   --video_file=test_video.mp4 \
+                                                   --device=gpu \
+                                                   --enable_attr=True \
+                                                   --model_dir det=ppyoloe/
+```
+
+The test result is：
+
+<div width="1000" align="center">
+  <img src="https://user-images.githubusercontent.com/48054808/159898428-5bda0831-7249-4889-babd-9165f26f664d.gif"/>
+</div>
+
+Data Source and Copyright：Skyinfor Technology. Thanks for the provision of actual scenario data, which are only used for academic research here.
+
+## Introduction to the Solution 
+
+1. The PP-YOLOE model is used to handle detection boxs of input images/videos from object detection/ multi-object tracking. For details, please refer to the document [PP-YOLOE](../../../configs/ppyoloe).
+2. Capture every pedestrian in the input images with the help of coordiantes of detection boxes.
+3. Analyze the listed labels of pedestirans through attribute recognition. They are the same as those in the PA100k dataset. The label list is as follows:
+```
+- Gender
+- Age: Less than 18; 18-60; Over 60
+- Orientation: Front; Back; Side
+- Accessories: Glasses; Hat; None
+- HoldObjectsInFront: Yes; No
+- Bag: BackPack; ShoulderBag; HandBag
+- TopStyle: UpperStride; UpperLogo; UpperPlaid; UpperSplice 
+- BottomStyle: LowerStripe; LowerPattern
+- ShortSleeve: Yes; No
+- LongSleeve: Yes; No
+- LongCoat: Yes; No
+- Trousers: Yes; No
+- Shorts: Yes; No
+- Skirt&Dress: Yes; No
+- Boots: Yes; No
+```
+
+4. The model adopted in the attribute recognition is [StrongBaseline](https://arxiv.org/pdf/2107.03576.pdf), where the structure is the multi-class network structure based on ResNet50, and Weighted BCE loss and EMA are introduced for effect optimization.
+
+## Reference
+```
+@article{jia2020rethinking,
+  title={Rethinking of pedestrian attribute recognition: Realistic datasets with efficient method},
+  author={Jia, Jian and Huang, Houjing and Yang, Wenjie and Chen, Xiaotang and Huang, Kaiqi},
+  journal={arXiv preprint arXiv:2005.11909},
+  year={2020}
+}
+```
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