yolov5-face-occlusion-project

• 연구 목표: YOLOv5 기반 얼굴검출 신경망을 사용하여, occlusion으로 얼굴 검출이 어려운 상황에서도 검출률을 올리는 방법을 연구한다.
• occlusion: 사람의 손이 가렸을 때를 가정, 1가지 손 사진을 이용하여 의도적으로 얼굴부분을 가린다. 얼굴과 손의 bounding box를 occlusion 정도로 정하여 가려지는 정도를 판단한다.
• dataset: widerface dataset 중 1사람만 있는 데이터 사용(label에 1명만 인식된 경우)
• 가장 mAP비율이 낮은 것을 train image에 50% 포함하여 재학습

Introduction

Yolov5-face is a real-time,high accuracy face detection.

Performance

Single Scale Inference on VGA resolution（max side is equal to 640 and scale).

Large family

Method	Backbone	Easy	Medium	Hard	#Params(M)	#Flops(G)
DSFD (CVPR19)	ResNet152	94.29	91.47	71.39	120.06	259.55
RetinaFace (CVPR20)	ResNet50	94.92	91.90	64.17	29.50	37.59
HAMBox (CVPR20)	ResNet50	95.27	93.76	76.75	30.24	43.28
TinaFace (Arxiv20)	ResNet50	95.61	94.25	81.43	37.98	172.95
SCRFD-34GF(Arxiv21)	Bottleneck Res	96.06	94.92	85.29	9.80	34.13
SCRFD-10GF(Arxiv21)	Basic Res	95.16	93.87	83.05	3.86	9.98
-	-	-	-	-	-	-
YOLOv5s	CSPNet	94.67	92.75	83.03	7.075	5.751
YOLOv5s6	CSPNet	95.48	93.66	82.8	12.386	6.280
YOLOv5m	CSPNet	95.30	93.76	85.28	21.063	18.146
YOLOv5m6	CSPNet	95.66	94.1	85.2	35.485	19.773
YOLOv5l	CSPNet	95.78	94.30	86.13	46.627	41.607
YOLOv5l6	CSPNet	96.38	94.90	85.88	76.674	45.279

Small family

Method	Backbone	Easy	Medium	Hard	#Params(M)	#Flops(G)
RetinaFace (CVPR20	MobileNet0.25	87.78	81.16	47.32	0.44	0.802
FaceBoxes (IJCB17)		76.17	57.17	24.18	1.01	0.275
SCRFD-0.5GF(Arxiv21)	Depth-wise Conv	90.57	88.12	68.51	0.57	0.508
SCRFD-2.5GF(Arxiv21)	Basic Res	93.78	92.16	77.87	0.67	2.53
-	-	-	-	-	-	-
YOLOv5n	ShuffleNetv2	93.74	91.54	80.32	1.726	2.111
YOLOv5n-0.5	ShuffleNetv2	90.76	88.12	73.82	0.447	0.571

Pretrained-Models

Name	Easy	Medium	Hard	FLOPs(G)	Params(M)	Link
yolov5n-0.5	90.76	88.12	73.82	0.571	0.447	Link: https://pan.baidu.com/s/1UgiKwzFq5NXI2y-Zui1kiA pwd: s5ow, https://drive.google.com/file/d/1XJ8w55Y9Po7Y5WP4X1Kg1a77ok2tL_KY/view?usp=sharing
yolov5n	93.61	91.52	80.53	2.111	1.726	Link: https://pan.baidu.com/s/1xsYns6cyB84aPDgXB7sNDQ pwd: lw9j,https://drive.google.com/file/d/18oenL6tjFkdR1f5IgpYeQfDFqU4w3jEr/view?usp=sharing
yolov5s	94.33	92.61	83.15	5.751	7.075	Link: https://pan.baidu.com/s/1fyzLxZYx7Ja1_PCIWRhxbw Link: eq0q,https://drive.google.com/file/d/1zxaHeLDyID9YU4-hqK7KNepXIwbTkRIO/view?usp=sharing
yolov5m	95.30	93.76	85.28	18.146	21.063	Link: https://pan.baidu.com/s/1oePvd2K6R4-gT0g7EERmdQ pwd: jmtk, https://drive.google.com/file/d/1Sx-KEGXSxvPMS35JhzQKeRBiqC98VDDI
yolov5l	95.78	94.30	86.13	41.607	46.627	Link: https://pan.baidu.com/s/11l4qSEgA2-c7e8lpRt8iFw pwd: 0mq7, https://drive.google.com/file/d/16F-3AjdQBn9p3nMhStUxfDNAE_1bOF_r

Data preparation

1. Download WIDERFace datasets.
2. Download annotation files from google drive.

cd data
python3 train2yolo.py /path/to/original/widerface/train [/path/to/save/widerface/train]
python3 val2yolo.py  /path/to/original/widerface [/path/to/save/widerface/val]

Training

CUDA_VISIBLE_DEVICES="0,1,2,3" python3 train.py --data data/widerface.yaml --cfg models/yolov5s.yaml --weights 'pretrained models'

WIDERFace Evaluation

python3 test_widerface.py --weights 'your test model' --img-size 640

cd widerface_evaluate
python3 evaluation.py

Test

Landmark Visulization

First row: RetinaFace, 2nd row: YOLOv5m-Face YOLO5Face was used in the 3rd place standard face recogntion track of the ICCV2021 Masked Face Recognition Challenge.

References

https://github.com/ultralytics/yolov5

https://github.com/DayBreak-u/yolo-face-with-landmark

https://github.com/xialuxi/yolov5_face_landmark

https://github.com/biubug6/Pytorch_Retinaface

https://github.com/deepinsight/insightface