- 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
Yolov5-face is a real-time,high accuracy face detection.
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 |
| 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 |
- Download WIDERFace datasets.
- 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]CUDA_VISIBLE_DEVICES="0,1,2,3" python3 train.py --data data/widerface.yaml --cfg models/yolov5s.yaml --weights 'pretrained models'python3 test_widerface.py --weights 'your test model' --img-size 640
cd widerface_evaluate
python3 evaluation.py
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.
https://github.com/ultralytics/yolov5
https://github.com/DayBreak-u/yolo-face-with-landmark
https://github.com/xialuxi/yolov5_face_landmark